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A NETWORK APPROACH TO THE ANALYSIS OF CITATION FLOWS: A COMPARATIVE STUDY OF TWO RESEARCH AREAS IN THE NATURAL AND THE SOCIAL SCIENCES
DISSERTATION
Presented in Partial Fulfillment of the Requirements for
the Degree Doctor of Philosophy in the Graduate
School of The Ohio State University
By
Stéphane Baldi, M.A.
*****
The Ohio State University 1997
Dissertation Committee: Approved by Professor Lowell L. Hargens, Adviser
Professor Robert L. Kaufinan Adviser Professor Barbara F. Reskin Department of Sociology tJMI Number: 9731586
UMI Microform 9731586 Copyright 1997, by UMI Company. All rights reserved.
This microform edition Is protected against unauthorized copying under Title 17, United States Code.
UMI 300 North Zeeb Road Ann Arbor, MI 48103 Copyright by Stéphane Baldi 1997 ABSTRACT
This dissertation conceptualizes the citation process as a dyadic relationship that simultaneously depends on characteristics of both citing and cited articles. Using data on two research areas, one in astrophysics and one in economics, this research develops a network-analytic approach to analyze all of the citation links from later papers to earlier papers in an area. Specifically, regression analyses examine the extent to which paper, author, and journal characteristics of both potentially citing and potentially cited papers infiuence the probability that a citation between the papers exists. Substantively, this project extends our understanding of stratification in science by assessing the relative worth of competing arguments on the use and fimctions of citations. Furthermore, a network approach permits the inclusion of variables indicating various relationships between citing and cited authors and papers so that 1 can test the argument that social ties between scientists influence their citation decisions. Finally, by adopting a comparative firamework, this dissertation tests hypotheses about variation in the allocation of citations across disciplines differentially located along a hard-soft dimension.
Results from both generalized least squares and logistic regressions identify strong effects of cited article quality and content but only weak or insignificant effects of functionally irrelevant characteristics of cited author such as institutional prestige or eminence. These findings tend to support a normative interpretation of the allocation of citations in which citations reflect payment of intellectual debt rather than a social constructivist interpretation in which citations are rhetorical tools of persuasion.
Furthermore, the lack of effects of social ties between citing and cited authors provides little support for the argument that authors who know one another are more likely to cite one another's work. Finally, consistent with much of the literature on disciplinary differences, my results suggest that the reward system is more universalistic and less dependent upon ascriptive characteristics in astrophysics than in economics. However, results from these two areas provide only weak support for the argument that the natural sciences differ from the social sciences in their pattern of rapid incorporation of new knowledge and cumulative building upon recent work.
Ill Pour Susanne, Giorgio et Marie-CIaude
IV ACKNOWLEDGMENTS
I wish to thank several people who gave me invaluable advice during the conduct of this research. Foremost among them is my adviser, Lowell Hargens, who not only throughout the writing of this dissertation, but throughout my entire career at Ohio State, provided timely feedback, constructive criticism, and intellectual support. Robert
Kaufinan also offered insightful comments and applied his infinite methodological wisdom to some of the more technical aspects of my analyses. Finally, Barbara Reskin was kind enough to make time for some pointed feedback at the early and late stages of this project.
Special thanks go to Jaya Sastry for taking an interest in my topic and being always willing to discuss my results and their implications. Thanks also to Karlyn Geis,
Lisa Morrison, Marieke van Willigen, O'Reilly's, and The Counterfeit Heist for extra curricular support.
Last but not least, thanks to Susanne Schmeidl for her love and patience.
This research was supported by National Science Foundation Dissertation
Improvement Grant SBR-9633763. Part of the data collection was supported by National
Science Foundation grant SBR-9223317. VITA
January 31,1969...... Bom - Paris, France
1992...... B.A. Sociology, University of Massachusetts at Boston
1992 -1994...... Graduate Teaching and Research Associate, University of Connecticut
1993 ...... M.A. Sociology, University of Connecticut
1994 - present...... Graduate Teaching and Research Associate, The Ohio State University
PUBLICATIONS
1. Baldi, S., and D. B. McBrier. 1997. "Do the Determinants of Promotion Differ for Blacks and Whites? Evidence from the U.S. Labor Market." Work and Occupations 24.
2. Baldi, S., and L. L. Hargens. 1997. "Reexamining Price's Conjectures on the Structure of Reference Networks: Results from the Special Relativity, Spatial Diffusion Modeling, and Role Analysis Literatures." Social Studies o f Science 27.
3. Baldi, S. 1997. "Departmental Quality Ratings and Visibility: The Advantages of Size and Age." The American Sociologist 28:88-100.
4. Baldi, S. 1995. "Prestige Determinants of First Academic Job for New Sociology Ph.D.s 1985-1992." The Sociological Quarterly 7>6'.111-1%9.
5. Baldi, S., and L. L. Hargens. 1995. "Reassessing the N-rays Reference Network: The Role of Self Citations and Negative Citations." Scientometrics 34:239-253.
VI 6. Baldi, S., and L. L. Hargens. 1995. "Reference Network Structure in Turn of the Century Physics: The Case of N-rays." In M. E. D. Koenig, and A. Bookstein (Eds.), Proceedings o f the Fifth Biennial Conference o f the International Society for Scientometrics and Informetrics, pp. 43-52. Medford, NJ: Learned Information.
7. Baldi, S. 1994. "Changes in the Stratification Structure of Sociology, 1964-1992." The American Sociologist 25:28-44.
FIELDS OF STUDY
Major Field: Sociology
Vll TABLE OF CONTENTS
Cage
Abstract ...... ii
Dedication...... iv
Acknowledgments...... v
Vita...... vi
List of Tables ...... xi
List of Figures...... xiii
Chapters:
1. Introduction...... 1
1.1 The role of citations in contemporary science...... 1 1.2 Citations as dyadic relationships...... 3 1.3 Main perspectives on the use and functions of citations...... 5 1.4 Disciplinary differences in citation patterns...... 8 1.5 Research questions...... 9 1.5.1 The role of citing article characteristics...... 10 1.5.2 Relative importance of article, author, and journal characteristics...... 10 1.5.3 The role of social ties...... 11 1.5.4 Field differences in citation patterns...... 12 1.6 Organization of the dissertation...... 13
2. Review of the literature...... 14
2.1 Perspective on citations...... 14 2.2 Citations as dyadic relationships...... 24 2.3 Variables Selection and Hypotheses...... 26 2.3.1 Article characteristics...... 27 2.3.2 Author characteristics...... 30
Vlll 2.3.3 Journal characteristics...... 32 2.3.4 Relational variables ...... 33 2.4 Why should the determinants of citations differ in the natural and the social sciences?...... 34 2.5 Hypotheses about disciplinary differences...... 40 2.6 Summary...... 41
3. Data and methods...... 43
3.1 The research areas...... 43 3.1.1 Celestial masers...... 47 3.1.2 Rational expectations...... 49 3.2 The sample...... 51 3.3 Network structure of the dataset...... 52 3.4 Description of the variables...... 53 3.4.1 The dependent variable...... 54 3.4.2 Characteristics of the article...... 54 3.4.3 Characteristics of the article's author(s)...... 58 3.4.4 Journal characteristics...... 62 3.4.5 Relational variables ...... 64 3.5 Missing data...... 65 3.6 Exploratory analyses...... 74 3.7 The regression model ...... 82
4. Analysis of citation patterns within celestial masers...... 88
4.1 Regression results...... 88 4.2 Discussion...... 97 4.3 Conclusion...... 101
5. Analysis of citation patterns within rational expectations...... 103
5.1 Regression results...... 103 5.2 Discussion...... 109 5.3 Conclusion...... 115
6. Comparing the determinants of citations in the natural and the social sciences 117
6.1 Comparing the overall structure of the areas...... 117 6.2 Do the determinants of citations differ among two research areas in the natural and the social sciences?...... 126 6.3 Conclusion...... 133
IX 7. Conclusion...... 135
7.1 Limitations of the research ...... 140 7.2 Contributions of the research ...... 143 7.3 Suggestions for future research and implications...... 144
Bibliography...... 147
Appendices
Appendix A. List of the 100 articles in the celestial masers sample...... 159
Appendix B. List of the 100 articles in the rational expectations sample 168 LIST OF TABLES la h k Eâgê
3.1 Variables list and operationalizations...... 66
3.2 Descriptive statistics for variables in 100 articles in celestial masers ...... 71
3.3 Descriptive statistics for variables in 100 articles in rational expectations...... 72
3.4 Descriptive statistics for dependent and relational variables for celestial masers...... 73
3.5 Descriptive statistics for dependent and relational variables for rational expectations...... 73
3.6 Exploratory factor analysis of article characteristics for celestial masers...... 76
3.7 Exploratory factor analysis of article characteristics for rational expectations .... 76
3.8 Exploratory factor analysis of author characteristics for celestial masers...... 77
3.9 Exploratory factor analysis of author characteristics for rational expectations.... 77
3.10 Exploratory factor analysis of j oumal characteristics for celestial masers...... 78
3.11 Exploratory factor analysis of j oumal characteristics for rational expectations ... 78
3.12 Exploratory factor analysis of relational variables for celestial masers ...... 79
3.13 Exploratory factor analysis of relational variables for rational expectations ...... 79
3.14 Descriptive statistics and reliability coefficients for the scales in celestial masers...... 81
3.15 Descriptive statistics and reliability coefficients for the scales in rational expectations...... 81
XI 4.1 One-way random effects EGLS regression results for celestial masers - Base m odel...... 91
4.2 Logistic regression results for celestial masers - Base model...... 93
4.3 One-way random effects EGLS regression results for celestial masers - Final model...... 94
4.4 Logistic regression results for celestial masers - Final model...... 96
5.1 One-way random effects EGLS regression results for rational expectations - Base model...... 105
5.2 Logistic regression results for rational expectations - Base m odel...... 106
5.3 One-way random effects EGLS regression results for rational expectations - Final model...... 108
5.4 Logistic regression results for rational expectations - Final model...... 110
6.1 Means comparisons for variables in 100 celestial masers and 100 rational expectations articles...... 121
6.2 Means comparisons of dependent and relational variables for 4950 pairs of potentially citing/potentially cited articles in celestial masers and rational expectations...... 122
6.3 Comparison of one-way random effects EGLS regression results for celestial masers and rational expectations...... 127
XU LIST OF FIGURES
Figure Page
3.1 Literature growth in celestial masers and rational expectations...... 45
Xlll CHAPTER I
INTRODUCTION
The Role of Citations in Contemporary Science
Since the introduction of citation indexes in the early 1960s, their use in evaluating individuals, departments, and even national scientific efforts has steadily expanded (Shadish, Tolliver, Gray, and Gupta 1995). A recent survey (Hargens and
Schuman 1990) reported that approximately sixty percent of graduate departments at U.S. universities use citation counts in making decisions about hiring, promotion, and tenure.
High-circulation publications such asScience and The Scientist frequently publish lists of
"hot papers," highly cited institutions, and rapidly-developing research areas — all defined by the fact that they have received large numbers of citations. Partially on the basis of citation counts, decisions are now made about downsizing departments, allocating prestigious awards, and funding given to a specific research area. Thus, the distribution of citations among individuals, departments, and institutions is an increasingly important fact of contemporary science. Yet despite the widely acknowledged importance of citations, many scholars have noted that we know little about the factors that influence whether a given paper, and therefore a given scholar, is cited (Cozzens 1989; Cronin
1984; Gilbert 1977; Kaplan 1965; Shadish et al. 1995).
1 The lack of attention given to the determinants of scientists' citation patterns is puzzling in light of the importance of citations not only for the development of science policy but for stratification in science. The metaphor of science as a conversation made up of individual scientists' contributions (e.g., papers, books) competing for attention
(Collins 1975) is particularly useful in understanding the role citations play. Careers in science are built upon the visibility of one's contributions. But the attention one receives is determined by other scientists, largely through citations. In other words, a scientific contribution does not become legitimized until it has been endorsed by other scientists through having been used, as indicated by having been referred to, more often that not in the form of a citation (Latour 1987). Scientists constantly make choices regarding what contributions they should accept as valid knowledge and these choices, reflected by citations, in turn determine the position of other scientists in the conversation of science.'
Citations are thus one of the critical micro-level stratifying mechanisms in science. If we are to understand the internal structure of science and the position of its members within it, then we must know the factors that influence scientists' citation patterns.
'Some past research has emphasized that not all citations are equivalent, or do not even reflect "valid knowledge," since many of them tend to be of a negative or critical nature (Baldi and Hargens 1995; Chubin and Moitra 1975; Moravcsik and Murugesan 1975). However, citation counts such as those compiled by the various citation indexes (e.g.. Social Science Citation Index; Science Citation Index), or those compiled by departments for tenure or promotion decisions do not differentiate the type of citation one receives, working instead under the assumption thatany citation is better than no citation at all. Thus one could argue that even "negative" citations are a form of credit that legitimize one's contribution. This is similar to Stigler's (n.d.) claim that in academia, the only bad press is an obituary. In this dissertation I assess the validity of existing arguments on the use and functions of citations. In order to estimate to what extent characteristics of both potentially citing and potentially cited papers influence the likelihood that a citation will be made I treat citations as dyadic relationships between citing and cited documents.
Studying the determinants of scientists' citation patterns will clarify the mechanisms by which an article and, by extension, its author, receives credit, or as Collins (1975) suggested, attention.
In addition, this dissertation uses data on two research areas, one in economics and one in astrophysics, to test arguments that the determinants of citations may vary across disciplines (Bazerman 1988; Cozzens 1985). Sociologists of science have increasingly emphasized how fields differentially located along a hard-soft dimension exhibit differences in the distribution of scientific rewards (Collins 1975; Hargens 1975;
Whitley 1984). This study will address these arguments by examining to what extent factors that influence citations in a natural science research area are similar to those in a social science research area.
Citations as Dyadic Relationships
The citation process involves a dyadic relationship between a potentially cited paper and a potentially citing one; there can be no "cited" paper without a "citing" paper as well. Yet past studies have explained the citation process only in terms of the characteristics of the article being cited (Shadish et al. 1995; Stewart 1983, 1990), ignoring the role played by the characteristics of the citing article. The failure of these studies to conceptualize citations as embedded within both a citing and a cited paper
(Small 1978) may have resulted in misspecified models of the citation process.
This dissertation avoids this misspecification by treating citations as dyadic relationships and therefore conceptualizing each dyad as one in which both the characteristics of the potentially citing paper and the potentiallycited paper account for the potential existence of a citation among the papers. My purpose is to examine to what extent article, author, and journal characteristics of both potentially citing and potentially cited papers influence the probability that a citation between the papers exists. Doing so will allow me to assess the validity of competing arguments on the use and functions of citations.
I estimate the effects of potentially citing and potentially cited articles on the occurrence of a citation by developing a network-analytic approach that maps all the citation links from later papers to earlier papers in a research area. A network approach improves upon past studies of citation determinants because it permits the inclusion of variables indicating various relationships between the potentially citing and potentially cited articles that have been thought to influence the existence of a citation between two papers. For example, scholars have long suspected that the existence of social ties between scientists (as colleagues or graduates from the same institution) influences their citing decisions (Edge 1977; Kaplan 1965). I can test this argument with a network approach that conceptualizes a citation as a dyadic relationship. Main Perspectives on the Use and Functions of Citations
Given the growing importance of citations as a tool for evaluation, scholars have speculated as to the determinants of citation flows. Two main positions have emerged in the debate over the role and functions of citations in contemporary science. One account is based on the assumption that science is a normative institution governed by internal rewards and sanctions (Merton 1973; Storer 1966). Scientists are believed to exchange information (in the form of publications) for recognition (in the form of awards and citations; Hagstrom 1965). This suggests that citations are a way to acknowledge intellectual debt (Kaplan 1965), and thus are mostly influenced by the cognitive, methodological, or topical content of the cited articles. This view is challenged by social constructivists who disagree with the view of science as an institution governed by a set of internally sanctioned norms. Instead, they argue that scientific knowledge is socially constructed through the manipulation of political and financial resources and the use of
rhetorical devices (Bloor 1976; Knorr-Cetina 1981,1995; Latour and Woolgar 1979).
Citations are one rhetorical device that scientists employ in order to provide support for
their papers and convince readers of the validity of their claims (Gilbert 1977; Latour
1987). According to this perspective, citations perpetuate and shape existing patterns of
institutional stratification, and are little more than appeals to existing authority on the part
of authors who wish to buttress their arguments. This view suggests that the factors that
influence citations have more to do with the location of a cited paper's author within the
stratification structure of science than with the cognitive contents of the article itself. As Stewart (1983) noted, these two perspectives can be seen as describing two different processes for the allocation of rewards — in this case, citations to articles — within science; achievement and ascriptive processes. To the extent that citations are distributed on the basis of what one says, as indicated by the cognitive, perceived quality, methodological, or topical contents of one's article ~ regardless of one's position in the stratification structure of his or her discipline — we tend to have an achievement process.
By contrast, if citations to one's article are distributed on the basis of who one is, as indicated by functionally irrelevant author characteristics such as one's eminence, rank, sex, or prestige of one's employing or doctoral institution — regardless of the quality or content of one's contribution — we tend have an ascriptive process. Of course, science, like most institutions, is likely to exhibit both of these processes. The key question, then, is the extent to which each of these two processes govern the allocation of citations.
Despite the obvious differences between the social constructivist and the normative accounts of citation practices, few empirical studies have assessed the validity of either claim — for good reasons. First, most past studies of the processes of allocation of rewards in science using citations as an indicator of such rewards have used the individual level of analysis in which the dependent variable is the number of citations received by a given author rather than by a given paper (e.g., Allison and Long 1990;
Bayer and Folger 1966; Cole and Cole 1973; Hargens and Hagstrom 1982). Yet, all articles by the same author are not equally important. The problem with an analysis using the individual author as the unit of analysis is that it is impossible to assess the effects of the cognitive, methodological, or topical content of the author's articles, thus making it impossible to determine the extent to which citations are awarded according to an achievement rather than an ascriptive process.
Second, the few studies that have used the article as the unit of analysis (e.g.,
Shadish et al. 1995; Stewart 1983,1990) have also been limited as to the extent to which they could test the prevalence of one system of allocation of reward over the other, largely because, as a result of their study design, they lacked a crucial indicator of the cognitive content of an article: its perceived quality. Although the views advanced by both camps regarding citation behavior are different, in some instances their implications for observable behavior may not be. For example, it is possible that scientists cite work to give recognition and acknowledge intellectual debt as well as to convince readers that their arguments are valid. However, the crucial difference between the two positions revolves around the role of the intellectual worth (or quality) of contributions. According to normativists, the perceived worth of a contribution is the crucial factor affecting whether it is subsequently used. In contrast, social constructivists claim that it is merely one of many factors that influence the allocation of rewards, but that it is by no means a crucial or necessary one.
By adopting a network analytic approach to the study of citations, in this dissertation I am able to obtain a measure of article quality by using number of times an article was cited three and four years after publication, excluding self-citations and, for a given tie, excluding the citation coming from the citing paper. The availability of this measure is an important improvement upon past studies in that it allows me to assess which of the two positions on citations predominates, as well as allowing me to assess the relative prevalence of an achievement versus an ascriptive process in the allocation of credit in science.
Disciplinary Differences in Citation Patterns
Building upon Popper's (1959, 1963), Merton's (1942), and Kuhn's (1970) demarcationist concerns with the question of why some fields make more progress than others, sociologists, historians, and philosophers of science have spent much time documenting how differentially organized knowledge leads to different outcomes in terms of the distribution of scientific rewards and the organization of scientific work (Collins
1975; Fuchs 1992; Hagstrom 1965; Hargens 1975; Pfeffer 1993; Price 1965,1970;
Whitley 1984). These scholars have given special attention to the division between the
"hard" and the "soft" sciences. Specifically, they argue that because the hard sciences are more codified and have greater consensus on what constitutes quality work than the soft sciences, their reward system is more universalistic — that is, more dependent upon "what one says" than on "who one is" (Collins 1975; Whitley 1984; Zuckerman and Merton
1971,1972). In terms of implications for citation patterns, this suggests that scientists working in the natural sciences might rely less on author characteristics and more on paper characteristics than those in the social sciences since quality work is easier to identify and less dependent on the prestige of its author(s).
In addition, studies of disciplinary differences in citation patterns find that authors in soft fields cite an older literature than those in hard fields, presumably as a result of lack of integration around recent research developments (Griffith and Small 1983; Line
8 1981). Cozzens (1985), for example, showed that scientists working in hard and soft disciplines tend to cite for different reasons, an argument supported by Bazerman (1988).
Scientists in the physical sciences tend to refer to specific knowledge claims in an article as well as to specific techniques employed, while those in the social sciences tend to refer to the general theme of an article. These arguments suggest that the technical and topical contents of an article is more important in the hard sciences. By comparing a research area in the natural sciences with one in the social sciences, this dissertation will attempt to test the extent to which differentially organized knowledge leads to different determinants of citation patterns.
Research Questions
Traditionally, sociological research has either been exploratory, descriptive, or hypothesis testing. The decision to explore or describe rather than test hypotheses depends on the state of empirical knowledge and the level of theoretical development in the area being studied. Exploratory research is an inductive process aimed at constructing theory rather than the testing of hypotheses. All research begins with exploration and description, but the testing of hypotheses presumes a basic level of empirical knowledge about the phenomena under study and a sufficiently well developed theory from which to draw testable hypotheses. Although much speculating has been done on the use and functions of citations, few — if any — of these speculations have resulted in a set of organized propositions that form a theory of citing. Studies providing empirical evidence for these speculations have been even more scarce. Although in chapter two 1 use past literature to formulate several specific hypotheses regarding the expected effects of given variables, the main research questions investigated by this dissertation are descriptive rather than hypothesis-testing.
The Jlole of Citing Article Characteristics:
The main goals of this research are to identify factors that lead to a paper citing another, and to assess the extent to which these factors are similar or different in the natural and social sciences. Since past research on the determinants of citations has ignored the role played by characteristics of the potentially citing paper, my first task will be to estimate the effects of these characteristics on the presence of a citation.
I. To what extent do measured characteristics of the potentially citing article and its corresponding author(s) and journal influence the probability that there will be a citation link between two papers?
Relative Importance of Article. Author, and Journal Characteristics:
Much of the debate over the use and functions of citations has focused on the issue of whether scientists cite in order to repay an intellectual debt (Kaplan 1965) or to buttress their argument by citing the most influential sources (Latour 1987:33-40).
Normative accounts, by emphasizing the repayment of intellectual debt, suggest that scientists tend to rely more on "what one says" (indicated by the content of one's article) than on "who one is" (indicated by author characteristics such as eminence or prestige of employing or doctoral institutions) in their citation practices. On the other hand, social
10 constructivist accounts, by emphasizing the role of citations as tools of persuasion, suggest that author characteristics are non-trivial determinants of what one cites.
The importance of journal characteristics is more difficult to assess because itIS subject to competing interpretations. Social constructivists argue that scientists overcite contributions published in high-quality journals in order to impress the reader and enlist the "support" of respected journals. This can be seen as an attempt on the part of authors to ally themselves with high-quality contributions to discourage "being sacked" by later papers (Latour 1987:31-33). Instead, proponents of the normative view argue that overcitation o f high-quality journals can be explained by the fact that these journals tend to publish the best, most significant papers in a field (Glenn 1971). Although for different reasons, both of these arguments predict a positive effect of journal quality on the presence of a citation. This underscores the non-exclusiveness of the two positions for observable behavior mentioned above. A central goal of this research will be to estimate the relative influence of article, author(s), and journal characteristics on the presence of a citation.
2. What is the relative importance of article, author(s) and journal characteristics of both potentially citing and potentially cited papers on the presence of a citation?
The Role of Social Ties:
Scholars have argued that even if citation patterns tend to follow a normative
model, various social or institutional ties among scientists might influence their citation
choices (Edge 1977; Kaplan 1965). Because past studies did not include information on
11 the potentially citing paper, they were unable to model the social ties between citing and cited authors. Adopting a network approach to the distribution of citations allows me to estimate the role social ties play on the presence of a citation link between two papers.
3. Do social ties among potentially citing and potentially cited authors increase the probability that there will be a citation link between two papers?
Field Differences in Citation Patterns:
Since Price's work (1963, 1965, 1970), increasing attention has been devoted to the empirical investigation of the similarities or differences between the natural and social sciences. Many have argued that fields differentially located along a hard-soft dimension should exhibit differences in the distribution of scientific rewards as a result of differences in the codification of knowledge and differences in the level of consensus over basic issues in a field (Hagstrom 1965; Hargens 1975; Pfeffer 1993; Storer 1967;
Whitley 1984; Zuckerman and Merton 1971,1972), while others have denied this
distinction (Gieryn 1995; Knorr-Cetina 1981). By comparing research areas in
astrophysics and economics, this dissertation will assess the extent to which factors that
influence citations in a physical science research area differ from those in a social science
research area.
4. To what extent do the determinants of citations differ across a natural science and a social science research area?
In sum, this research will provide important information about the citation process
by ( 1 ) identifying factors that lead to a citation, (2) determining the extent to which social
ties influence citation choices, and (3) assessing whether the determinants of citations
12 differ across a "hard" and a "soft" research area. Answers to these questions are a much needed first step toward a theory o f scholarly recognition.
Organization of the Dissertation
In chapter two I review the existing literature that bears upon the research questions investigated by this dissertation. I first review the various theoretical and empirical perspectives about the use and functions of citations. I then develop a theoretical argument for treating citations as dyadic relationships, justify the variables included in the analyses, and conclude by reviewing the literature that leads me to expect differences in the determinants of citations in the natural and social sciences.
Chapter three describes the data and methods used in this dissertation. I discuss the choice of the areas, the sampling procedures, the network structure of the dataset, the operationalization of variables, the missing data handling, and the statistical estimation procedures.
Chapters four through six report the results of the analyses to answer the research questions raised at the beginning of this dissertation. Chapter four reports results of the analyses for celestial masers, the astrophysics research area. Chapter five reports results of the analyses for rational expectations, the economics area. In chapter six I compare the results obtained for both research areas and discuss their implications in light of the literature on disciplinary differences reviewed in chapter two. Chapter seven summarizes the overall results of this dissertation in relation to the research questions investigated and offers concluding remarks.
13 CHAPTER 2
REVIEW OF THE LITERATURE
Although many studies have used citation counts in their analyses, relatively few studies have explored the use and functions of citations and even fewer have attempted to identify empirically their determinants. In this chapter I review the theoretical and empirical literature on the use and functions of citations to provide a theoretical framework for the analyses in the rest of this dissertation. I develop an argument for treating a citation as a dyadic relationship that builds upon the work of Small (1978) and then formulate hypotheses about the expected effects of the variables included in the analyses. I conclude by reviewing the literature on disciplinary differences that leads me to expect differences in the determinants of citations between the natural and the social sciences.
Perspectives on Citations
Citation studies have examined both the macro and the micro level and have followed a variety of approaches. Macro-level studies are concerned with understanding how citations reflect the organization of science and diffusion of scientific knowledge, while micro-level studies tend to treat citations as the unit of analysis in order to elucidate
14 the contextual meaning of the citation process. At the macro level, past studies have either used citations as an indicator of eminence (Allison and Long 1990; Bayer and
Folger 1966; Cole and Cole 1973; Hargens and Hagstrom 1982; Long, Allison, and
McGinnis 1979; Zuckerman 1977), as a tool to develop maps of the structure of scientific specialties and disciplines (Crane and Small 1992; Griffith, Small, Stonehill, and Dey
1974; Line 1979, 1981; McCain 1984; Mullins, Hargens, Hecht, and Kick 1977; Small
1977; Small and Griffith 1974; White and Griffith 1982), or as a mean to assess the usefulness of citation indexes in the process of information retrieval (Cleverdon and
Kidd, 1976; Earle and Vickery 1969; Lipetz 1962, 1965).
Overall, macro-studies of citations have not been very useful in clarifying the role and functions of citations, largely because they did not conceptualize citations as a form of credit subject to competition for their allocation, but rather as a mere indicator of either scientists' eminence, disciplinary boundaries, or the efficiency of information retrieval systems.
The concern with the use and functions of citations, or what 1 will call scientists' citation behavior, emerged only at the micro level, first in studies of the context of citation usage, and then in speculative debates over why scientists use citations. The increasing use of citation counts as indicators of the "quality" of scientists' contributions
(Cole and Cole 1973) led critics to argue that such an approach was questionable because citations were not all of the same type (Chubin and Moitra 1975; Lipetz 1965; Moravcsik and Murugesan 1975; Spiegel-Rosing 1977; Tagliacozzo 1977). For example, critics argued that a work can be cited perfunctorily, that is without playing a real function in the
15 citing paper, or because it is wrong, so citation counts are flawed indicators of the
"quality" of scientists' work. These critics developed typologies of various kinds of citations through content analysis and showed that many citations are not of a "positive" or "functional" nature (Chubin and Moitra 1975; Moravcsik and Murugesan 1975).
Between 1965 and 1979 classificatory schemes trying to capture the various reasons why scientists cite a given work became a virtual cottage industry, producing no less than ten different classifications (Chubin and Moitra 1975; Finney 1979; Frost 1979;
Hodges 1978; Lindsey and Lindsey 1978; Lipetz 1965; Moravcsik and Murugesan 1975;
Oppenheim and Renn 1978; Thome 1977; Weinstock 1971). Most of these schemes had anywhere from four to twenty-nine categories capturing various reasons for citing, ranging from "historical importance" to "future research" and including "illustration,"
"disclaiming," "definition," "deliberate premeditation," "citations as reflection of author biases" and so on. Aside from the fact that these typologies appear to have been designed in virtual isolation of one another and in an ad-hoc fashion,' their development also tended to lead to cumbersome and lengthy analyses of only limited use, and few such studies have appeared since the 1970s. Yet these studies are important in underscoring the contextual nature of citations and bringing data to bear on the issue of citation behavior.
As Cronin (1984) noted, despite the idiosyncratic nature of these classificatory schemes, most of the reasons invoked by these studies for why scientists cite either imply
'In none of these studies did the authors attempt to determine the reasons underlying the choice of citations by asking scholars actively engaged in research. Instead, they seem to have based their schemes purely upon self experience.
16 some type of merit relationship between the citation and the cited article (e.g.,
"illustration"), or some type of misappropriation of the cited claim by the citer (e.g.,
"citations as reflection of author biases"), and tend to follow one of two different accounts of scientists' use of citations. The first account of citation behavior is based on the assumption that science is a normative institution governed by internal rewards and sanctions (Barber 1952; Hagstrom 1965; Merton 1973; Storer 1966). According to this perspective, scientists are supposed to be universalistic in their evaluation of contributions and not be influenced by functionally irrelevant characteristics such as scientists' race, sex, or rank. Specifically, Merton (1973) argued that the evaluation of scientific contributions is governed by a set of norms that involve the open communication of ideas, emotional neutrality in the evaluation of one's ideas, and the acknowledgment of intellectual debt to a piece of scholarship. As Hagstrom (1965) put it, scientists exchange information (in the form of publications) for recognition (in the form of awards and citations). This normative account sees the distribution of citations in a scientific paper as a reflection of the worth of contributions. Kaplan (1965) exemplified this view when he wrote:
The citation practices of scientists today are in large part a social device for coping with problems of property rights and priority claims....The citation is probably among the more important institutional devices for coping with the maintenance of the imperative to conununicate one's findings freely as a contribution to the common property of science while protecting "individual rights" with respect to recognition and claims to priority. (181)
Social constructivists challenge the view of science as an institution governed by a set of internally sanctioned norms. Instead, they argue that scientific knowledge is
17 socially constructed through the manipulation of political and financial resources and the use of rhetorical devices (Bloor 1976; Edge 1977; Knorr-Cetina 1981, 1995; Latour and
Woolgar 1979; Mulkay 1979). This suggests a different interpretation of scientists' citation behavior. Instead of scientists using citations to impart recognition and protect
"property rights," social constructivists portray scientists as using citations as tools of persuasion (Gilbert 1977; Latour 1987; Latour and Woolgar 1979). Proponents of this view claim that scientists carefully select citations to provide support for their papers and convince readers of the validity of their arguments (Gilbert 1977). Latour (1987) carried the constructivist argument furthest by likening the use of citations to a game and suggesting that the "choice" of citations is purely political:
Like a good billiard player, a clever author may calculate shots with three, four or five rebounds. Whatever the tactics, the general strategy is easy to grasp: do whatever you need to the former literature to render it as helpful as possible for the claims you are going to make. The rules are simple enough: weaken your enemies, paralyse those you cannot weaken..., help your allies if they are attacked, ensure safe communications with those who supply you with indisputable instruments..., oblige your enemies to fight one another; if you are not sure of winning, be humble and understated. These are simple rules indeed: the rules of the oldest politics. The result of this adaptation of the literature to the needs of the text is striking for the readers. They are not only impressed by the sheer quantity of references; in addition, all of these references are aimed at specific goals and arrayed for one purpose: lending support to the claim. (37-38)
Despite the obvious differences between the social constructivist and the normative accounts of citation practices, only two studies have attempted to identify empirically the determinants of scientists' citation flows. They have obtained mixed results. The first of these studies, conducted by Stewart (1983, 1990), was a giant step forward in the effort to clarify scientists' use of citations. Stewart sought to explain the
18 extent to which the number of citations a geoscience article received three years after its publication (identified as an article's peak citation time by the 1978 Science Citation
Index) was a result of article (length, number of references, number of authors, relevance of article to research area) versus author characteristics (institutional prestige, pre-article eminence, rank). Building upon the normative approach, Stewart argued that the allocation of citations followed two basic processes; achievement and ascriptive processes. To the extent that citations are allocated based on the cognitive content of an article, we have an achievement process, but if citations are determined partly by functionally irrelevant author characteristics (such as professional affiliation or age) net of the article’s cognitive content, then we have an ascriptive process.
Stewart's work was largely exploratory and did not test specific hypotheses. For this reason, he included many variables that could reasonably influence citations although he had no real theoretical justification for including them. Specifically, he included variables he claimed captured important aspects of articles such as article length, number of references, tables, graphs and equations, and variables indicating analytical techniques and general specialty subjects. He also included author characteristics such as rank, university affiliation, author eminence, productivity, and professional age. Stewart
(1983) found that article characteristics were much stronger predictors of citation counts than author characteristics, concluding that this provided evidence for the prevalence of an achievement system of stratification in science (Stewart 1983). Specifically, article length, as measured by number of pages, subtopic indicators, and number of references
19 were the strongest determinant of the number of citations an article received. In contrast, the majority of author variables had weak or insignificant effects.
While Stewart's work is inventive and useful, it only takes us part way toward an understanding of scientists' citation behavior. First — and most importantly — his analysis ignores a potentially crucial component of what can account for citation counts: namely the role played by the characteristics of the citing article and its author(s) and journal.
Citations do not exist until a citer decides to use a given article, and it is unreasonable to assume that the characteristics associated with that citer (and his/her citing article and journal) are irrelevant to his or her decision to cite. For example, the citer's collegial ties may influence the citation decision. When confronted with two potential choices, the citer may be more likely to cite the contribution of a mentor or student than that of an unknown scholar. This example emphasizes the fact that the process of citing involves a dyadic relationship between a potentially cited paper and a potentially citing one, with each of these, in turn, having a corresponding author and journal. Thus, explaining the citation process in terms of only one element of this dyad (i.e., the cited paper and its related author and journal characteristics) is likely to lead to a misspecified model.
Of course, Stewart's inability to include citer characteristics is directly tied to his choice of citation counts as the dependent variable. By choosing citation counts, Stewart restricted himself to explaining scientists' citation patterns only in terms of the cited
article. This approach made it impossible to link potentially important characteristics of
the citing articles since the unit of analysis was not the citation itself, but rather the cited
paper. For example, the effects of citing article length, topical contents, or citing author
2 0 employment prestige could not be estimated since Stewart had no data on the citing articles. Furthermore, the lack of information on the potentially citing paper also made it impossible to assess the influence of relational variables such as social ties (as either co worker or graduates from the same institution) among the potentially citing and potentially cited authors on the existence of a citation. Stewart's choice of design also prevented him from including a measure of article quality that would be independent of his dependent variable (since one could argue that his dependent variable is "article quality"), thus seriously limiting his ability to test the prevalence of an achievement process over an ascriptive one. In order to be able to estimate the effects of social ties and article quality, as well as those of the characteristics of the citing article and its corresponding author(s) and journal, one would have to use whether or not an article is cited as the dependent variable.
As a result of being confined to a single research area, geoscience, a second limitation of Stewart's work is its inability to address the question of whether the relative importance of social and intellectual resources varies across scientific fields. Research on disciplinary variation in the organization of science suggests that there may be differences in the determinants of citations across disciplines as a result of differences in the level of paradigm development, codification, and consensus (Hargens 1975; Lodahl and Gordon
1972; Pfeffer 1993; Zuckerman and Merton 1971, 1972).
The second empirical study of the determinants of scientists' use of citations was conducted by a team of psychologists who explored the meaning of citations through a survey (Shadish et al. 1995). After selecting all empirical and theoretical scientific
2 1 articles published in 1985 in three top psychology journals,^ they randomly chose one reference from each article and sent a 28-item questionnaire to its author, asking about his or her single best reason for using that specific reference (out of the list of 28 possible reasons). The 28 possible reasons for citing fell into six categories developed through factor analysis: negative citation; personally influential citation; creative citations; classic; citations for social reasons; and supportive citation. The modal response, checked by 18 percent of the authors sampled, was that "this reference supports an assertion in the sentence in which it occurred" (p. 483). The response with the second highest frequency (16 percent) was that "this reference documents the sources of a method or design feature used in your study" (p. 483). By contrast, not a single author
answered that they chose the reference for "social" reasons (i.e., because it was published
in a prestigious journal or authored by an influential author). The rest of the questions
were checked by anywhere between 0 and 4 percent of the respondents, with the
"personally influential citation" category getting most of the responses. Although this
study was purely exploratory and descriptive and did not draw on existing theories of the
use and functions of citations, its results support the normative interpretation of citation
use. However, one could argue that scientists may not be fully aware of, or candid about,
why they cite a given contribution, and a survey might only reflect post-hoc
rationalizations.
'The three Journal they chose were {{) American Psychologist, (2) Journal o f Consulting and Clinical Psychology, and (3) Psychological Bulletin.
2 2 A survey approach that asks citers their reason(s) for citing raises the question of whether it is ever possible to determine the "real" cause of citations if the citers don't know or are unwilling to admit why they cited what they did. It is likely that if one cited mostly professional acquaintances, or cited articles based on whether they were published in top journals, regardless of the relevance of the citation to his or her work, he or she might be hesitant to admit it. Furthermore, a survey approach that focuses on the single best reason for citing, as Shadish et al. (1995) did, ignores the fact that authors often have more than one equally valid reason for citing what they do, or that there could be secondary reasons. For example, it is possible that when having to choose among articles of equal quality or intellectual influence, social ties would then have an effect (i.e., the author would pick the article by someone they have personal ties with).
By adopting a social network approach that combines information from both potentially citing and potentially cited articles, I avoid some of the limitations associated with a survey approach (e.g., Shadish et al. 1995) while extending the potentials of a behavioral analysis confined to cited articles only (e.g., Stewart 1983,1990). As mentioned above, in this study I carry out a behavioral analysis of the correlates of whether a citation links any pair of papers in a research area. Stewart's study (1983,
1990) illustrated the advantages of using such a behavioral approach. The main advantages of a behavioral approach is that it does not rely upon the citers' post-hoc motives to identify the correlates of citation links, yet at the same time allows the researcher to identify multiple determinants of such links. Specifically, a behavioral
23 approach allows the assessment of therelative importance of various potential determinants of citations.
While Stewart's study (1983, 1990) was limited to explaining citation determinants in terms of characteristics of the cited article as a result of choosing articles as his unit of analysis, my choice of the presence or absence of a citation as the unit of analysis allows me to estimate the effects of several new sets of potential determinants of citation links among articles: namely, the effects of characteristics of the potentially citing articles and the effects of the presence of various social ties among the potentially citing and potentially cited authors (as ex co-students or colleagues at the same department). Furthermore, my design allows me to include a measure of the quality of the cited article, a crucial variable in assessing the relative prevalence of achievement versus ascription in the allocation of credit, yet one that has not been included in past studies. Thus, this dissertation improves upon past behavioral studies of citations while avoiding the limitations associated with a survey approach.
Citations as Dyadic Relationships
In this dissertation I argue that a more fruitful approach to understanding why scientists cite what they cite is to examine the factors that affect the likelihood of an article being cited in terms of the characteristics of both potentially cited and potentially citing articles, as well as the characteristics associated with these articles' authors and journals. I build upon a network-analytic model to map all the possible citation links
24 between later and earlier papers in a research area, using whether a citation occurs between a pair of papers as the dependent variable.
The concept of the citation as a dyadic relationship is implicit in most studies of the structure of science based on citation links between citing and cited papers (e.g., co citation studies), but the implications of this conceptualization for future research were not made clear until the publication of Small's (1978) article "Cited Documents as
Concept Symbols." Small argued that citation studies have ignored the role citations play as symbols of concepts or methods. In his view, citations are the main connection between an idea in the citing text and the cited document. Traditional interpretations of citations presented references as the sources upon which the author draws to give meaning to his or her text. Small reversed this interpretation by suggesting that the citing author also imparts meaning to the sources by citing them: "...[a citation] constitutes the author's interpretation of the cited work. In citing a document an author is creating its meaning, and this...is a process of symbol making." (p. 328) Thus, concluded Small, one cannot understand the existence of a citation without conceptualizing it as depending upon both a citing and a cited document.
Small's (1978) argument carries serious implications for research on the determinants of citations that subsequent studies ignored. The central implication is that research on the determinants of citations that fail to conceptualize the citation process as
involving a dyadic relationship between a citing and a cited paper is unlikely to properly model the factors that influence why scientists cite some articles over others. Since the
meaning of a citation is partly created by the citing author's interpretation of it,
25 information on the characteristics of the citing article and its related author and journal may provide important information in an attempt to identify the determinants of citations.
Variables Selection and Hypotheses
Although there has been little empirical research on the determinants of citations to suggest a set of well established variables to include in quantitative models of the allocation of citations, past research in the broader area of the sociology of science provides some theoretical guidance as to some of the variables that should be included.
In this section I use this literature to Justify the inclusion of variables and formulate hypotheses as to their expected effects. Overall, most of the predictions involving potentially cited articles tend to reflect the "attractiveness" of these articles, or what can be called their propensity to attract citations. In contrast, most of the predictions about potentially citing articles reflect ideas about what kind of papers contain the most references, or what can be called their propensity to cite other papers. Due to the largely exploratory nature of this research, I also include many variables that could reasonably lead to a citation, although I do not have strong theoretical justifications for their inclusion or clear hypotheses as to their expected effects.
In this dissertation, I model the likelihood of a citation occurring between a
potentially citing and a potentially cited article as a function of (1) article characteristics,
(2) author characteristics, (3) journal characteristics, and (4) relational variables
indicating the presence or absence of various ties among the potentially citing and
potentially cited articles. Furthermore, article, author, and journal characteristics exist for
26 both the potentially citing and the potentially cited articles. Consistent with past research on the determinants of citations (Stewart 1983, 1990), I argue that to the extent that citations are allocated largely on the basis of the cognitive contents of the article, we have a achievement process. In contrast, when citations are allocated mostly based on functionally irrelevant author characteristics such as sex, rank, or past eminence, net of the worth of the article, we have an ascriptive process.
Article Characteristics
In his study of the determinants of citations in geology, Stewart (1983,1990) found that indicators of article length such as number of pages and number of references had the strongest effects on citation counts. Abt (1981) also found that longer articles tended to be most often cited in the field of astronomy, possibly because they carried more information. Article that are longer have more opportunities to cite other articles.
Similarly, there is more in them to be cited. For these reasons, I expect indicators of article length to have a positive effect on the likelihood of a citation existing, both for the potentially citing and the potentially cited article. In this study I include two variables that seem to capture the length or scope of an article: the number of pages and the number of references. I also include variables indicating whether an article is a review of the literature, and whether it is published in an edited volume. Review articles tend to be longer articles and this should increase their likelihood both of citing and of being cited.
Furthermore, papers published in edited volumes are more likely to be review articles or overviews of research areas with more references than paper published in refereed
27 journals - at least in the hard sciences (Abt 1995). This should increase their likelihood of citing, although it is unclear whether it would increase their likelihood of being cited.
In science, where the production of new knowledge is at a premium (Merton
1957; Price 1965,1970), we should expect contributions that build upon recent knowledge to be more useful to other scientists than contributions recycling old ideas.
For this reason, I expect articles building upon recent knowledge to be more likely to be cited. I include two measures of the extent to which an article builds upon recent knowledge: the proportion of total references that are to articles published within the past three years, and the proportion of total references that are to forthcoming work.
Glenn (1971) showed that most scientists believe that on average the top journals publish the best work. Thus it makes sense that they should cite other articles that also make extensive use of articles published in the top journals, since a list of references containing many top-joumal articles probably indicates that the article builds upon the best work available. Referring to top journals is also consistent with a constructivist interpretation of citations. Scientists may cite articles published in top journals because they believe they will be more influential in convincing readers of the validity of the argument being made (Latour:31-32). These arguments suggest that the proportion of references to top journals should increase the likelihood of an article being cited.
However, 1 do not expect this variable to have an effect for the potentially citing article.
Much of the debate over the use and functions of citations has revolved around the issue of article "quality" (see Cole 1992; Cole and Cole 1973; Kaplan 1965; Latour 1987;
Zuckerman and Merton 1971). To boil down the main arguments, normativists argue that
28 an article's quality is a crucial determinant — if not the main determinant — of whether it gets cited (Cole and Cole 1973; Kaplan 1965), while social constructivists claim that the quality of an article is socially negotiated and constantly shifting and thus cannot explain why a given article is cited (Gilbert 1977; Latour 1987). Rather, they point to the crucial role of an author's position in the institutional structure of his or her discipline in influencing the citation process. In this dissertation I use a measure of cited article quality in order to assess these two arguments. If the normativists are correct, then potentially cited article quality should have one of the strongest — if not the strongest — effect on the likelihood of a citation existing. In contrast, if the social constructivists are right, then cited article quality should have a small or even insignificant effect. I also allow the quality of the citing article to predict the presence of a citation link between two papers although I have no clear expectations as to its effect.
In an analysis of the contents of papers published by economists, Leontief (1983) argued that leading economics journals such as theAmerican Economic Review put a premium on publishing theoretically sophisticated papers devoid of empirical evidence compared to papers reporting empirical results. This suggests that the theoretical and methodological contents of an article might influence which articles get cited. Although I formulate no directional hypotheses as to their effects, in this study 1 include several indicators of the theoretical and methodological content of an article: the number of quantitative tables and graphs per page, the number of equations per page, the number of theoretical figures per page, and whether an article claims to make a theoretical contribution to the literature in its field.
29 Finally, I also include variables indicating the number of authors who contributed to the article, and whether an article is critical of the literature in its field, even though I formulate no hypotheses as to their expected effects for the potentially citing or the potentially cited article.
Author Characteristics
The importance of university prestige on the distribution of rewards has been well documented. Graduates from prestigious institutions tend to obtain better jobs which they can translate into greater productivity and subsequent job mobility (Baldi 1995; Cole and Cole 1973; Crane 1965; Hargens and Hagstrom 1982; Long et al. 1979).
Furthermore, because of their central position in the information network of their discipline and their greater visibility, scientists working at highly prestigious institutions tend to receive a disproportionate amount of credit in science (Merton 1973; Zuckerman
1977). Thus I expect indicators of an author's Ph.D. and current employment prestige to have a positive effect on the likelihood of an article being cited. However, I make no prediction as to the effect of these variables for the potentially citing paper.
Stewart (1983) found that papers by eminent scientists were more likely to be cited than papers by their little-known colleagues. This is to be understood in terms of
Merton's (1968) theory of accumulative advantage. Scientists attempt to minimize the amount of time they spend retrieving information useful for their work. This means that they look for shortcuts to the best or most useful contributions. Under the assumption that eminent scientists tend to produce better work than little-known scholars, one way to
30 narrow the search is to pay greater attention to contributions by already established scientists. This tends to further solidify the position of established scientists since most scholars tend to pay greater attention to their work (hence the accumulative advantage), but it also insures that the most significant work gets communicated in the most efficient way (Merton 1968). This argument suggests that work by eminent scientists will be cited more. Here I include several measures that might capture the extent to which an author is known in his or her field: an author's citation count in the year prior to the article's publication; the percentage of authors of a given article who are full professors; and the author's professional age. In light of the above argument, I hypothesize that these measures will have a positive effect on the presence of a citation for the potentially cited paper. There is no literature that suggests potential effects of these variables for the potentially citing authors. I also include a variable indicating the number of previous articles an author published in an area under the assumption that scientists pay greater attention to authors who publish many articles in a specific research area than to authors who are only infirequent contributors to the development of the area. This variable should also have a positive effect on the presence of a citation, but only for the potentially cited paper.
The average female scientist receives fewer citations than the average male scientist and this finding persists across disciplines and through time (Fox 1987; Long
1990; Ward, Gast, and Grant 1992). Thus 1 expect a high percentage of female authors on a potentially cited article to decrease the likelihood of citation existing between two
31 papers. However, I have no clear expectations as to the effect of the percentage of female authors in the potentially citing paper.
Finally, scientists working in university settings are more likely than their non university colleagues to be at the center of the information and communication network of their field and thus more likely to produce work that is noticed and hence subsequently cited, as well as more likely to be aware of existing work (Stewart 1983). For this reason
I included a measure of the percentage of authors employed by a university and expect it to have a positive effect for both the potentially citing and the potentially cited papers.
Journal Characteristics
Although no previous study assessed the role of journal characteristics on subsequent citations, it is likely that articles published in journals with a wide circulation are cited more often than articles appearing in journals that only have a small circulation since scientists are more likely to own them. Similarly, since most scientists believe that top journals tend to publish the best articles (Glenn 1971), we should expect articles published in top journals to be cited more frequently. Thus I include measures of journal quality and circulation and, for the potentially cited article's journal, 1 expect them both to have a positive effect on the presence of a citation, though I make no such predictions for the characteristics of the potentially citing article's journal. It is likely that circulation and quality might be highly correlated, since we would expect better journals to also have higher circulations.
32 I also include variables indicating the journal's ownership (whether it is owned by a professional association or not), even though I have no clear expectation as to its effect.
Relational Variables
My network approach to citation flows allows me to include a set of relational variables that indicate various ties between the potentially citing and the potentially cited articles and their corresponding authors and journals.
Scholars have speculated as to the role social ties play upon subsequent citations.
Early on, Kaplan (1965) suggested that scientists might feel compelled to cite the work of their institutional colleagues. More recently, work on research schools (Geison and
Holmes 1993) suggests that individuals trained at the same university or by the same
mentor may be more likely to use each other's work. A social network approach allows
me to assess empirically whether the presence of various ties among authors increases the
likelihood that a citation exists. I include relational variables indicating whether any of
the potentially citing and potentially cited articles' authors are the same person, ever
worked at the same institution, or received the Ph.D. from the same graduate department,
and expect each of these variables to have a positive effect on the presence of a citation
between two articles.
In his study of citation determinants, Stewart (1983, 1990) found that articles were
more likely to cite other articles on the same specific topic than outside of it. This
suggest that a variable indicating whether the potentially citing and cited article are on the
same subtopic should have a positive effect on the presence of a citation.
33 In order to test Price's (1965, 1970) argument that scholarly literatures in the natural sciences are more likely to build upon recent work than scholarly literatures in the social sciences, I included a measure of years elapsed between the potentially citing and the potentially cited articles. Price's argument, which 1 will review in the following section, suggests that the time gap between citing and cited articles should have a strong negative effect on the presence of a citation in astrophysics, but a weak or insignificant effect in economics.
Finally, because they could plausibly influence the likelihood of a citation existing between two articles, 1 include variables indicating whether both the potentially citing and potentially cited articles (1) claimed to make a theoretical contribution, and (2) were published in the same journal, although I do not formulate specific hypotheses has to their expected effects.
Why Should the Determinants of Citations Differ Across the Natural and the Social
Sciences?
The interest in demarcating scientific fields can be traced back to Popper's (1959,
1963) concern with determining why some research areas like theoretical physics made progress while others like Marxist theory or Freudian psychoanalysis did not. The demarcationist enterprise was elaborated by the work of Merton (1942, 1973) and Kuhn
(1970) who spent much effort theorizing about the differences between, scientific, pseudo-scientific, and non-scientific activities, and about the institutional factors that
34 helped give legitimacy and validity to these differences.^ Much of this early work was theoretical and speculative, and not until the 1960s did scholars carry out systematic studies geared at empirically demonstrating the existence of such differences as well as documenting the extent to which disciplines vary along several dimensions (e.g.,
Hagstrom 1965; Price 1963, 1965, 1970).
In two pioneering and highly influential papers,'* Price argued that differences across scholarly literatures are reflected by the extent to which scientists cite the most recent work in their field. Price hypothesized that the hard sciences exhibit a pattern of cumulative growth wherein scholars build upon the most recent work as a result of competition for priority among researchers, scholarly fashion, and "invisible colleges."
This pattern, he argued, is characteristic of "research front" science where discoveries become rapidly incorporated into the stock of a discipline's common knowledge. In contrast, when a literature's references are equally likely to cite old as well as recent work, one has a purely archival field possessing a "humanistic type of metabolism in which the scholar has to digest all that has gone before, let it mature gently in the cellar of his wisdom, and then distill forth new words of wisdom about the same sorts of questions"
(Price 1970:15). This pattem. Price argued, was representative of the humanities. Price further hypothesized that the pattem exhibited by social science literature should fall
^For a recent review of the demarcationist tradition, see Gieryn (1995).
‘‘Price, D. J. de Solla. 1965. "Networks of Scientific Papers." Science 149:510- 515; and 1970."Citation Measures of Hard Science, Soft Science, Technology and Non- Science." In C. E. Nelson, and D. K. Pollock (Eds.), Communication among Scientists and Engineers, pp. 1-15. Lexington, MA: D. C. Heath.
35 between those he described for the hard sciences and the humanities. Although few scholars have tested Price's arguments, two recent studies by Baldi and Hargens (1995,
1997) suggest that fields identified as "hard" tend to cite more recent work than "softer" fields. Evidence from these and Price's original studies lead me to expect differences in the importance of literature aging across fields differentially located along the hard-soft dimension.
The work of Zuckerman and Merton developed another set of reasons for the existence of other disciplinary differences. Echoing Storefs (1967, 1972) argument that much interdisciplinary variation could be explained in terms of a hard-soft dimension distinguished by the amount of agreement upon the "rules" of research, Zuckerman and
Merton (1971,1972) argued that patterns of variation across disciplines are due to the extent of agreement on staridards of scholarship. Differences in levels of agreement across disciplines are a function of their respective levels of "codification" — the consolidation of empirical knowledge into succinct and interdependent theoretical formulations. Since the hard sciences exhibit greater levels of codification, their stratification pattem is less rigid than that of the social sciences because scholars are better able to agree on standards of scholarship and identify work that constitute significant contributions.
The articles by Zuckerman and Merton (1971,1972) were influential in encouraging further research on the relationship between consensus and the reward system of science across disciplines (Cole 1979; Cole, Cole, and Dietrich 1978; Gaston
1978; Hargens 1975, 1988; Hargens and Hagstrom 1982; Hargens and Kelly-Wilson
36 1994; Lodahl and Gordon 1972; Pfeffer 1993; PfefFer, Leong, and Strehl, 1977; Whitley
1984). The central argument motivating much of this research is that stratification patterns of low-consensus fields tend to be more rigid than those of high-consensus fields as a result of a lack of clear standards of scholarship and agreement over what constitutes quality or significant work. Although not all of these studies found evidence of a positive effect of consensus on fluidity in stratification patterns, the bulk of them support the argument that scientists working in low-consensus fields are more likely than those in high-consensus fields to rely on particularistic criteria such as scientists' eminence or prestige of their institutional afiiliations rather than universalistic ones like productivity when awarding credit (Hargens 1969; Hargens and Hagstrom 1982). Furthermore, scholars also found some evidence that hard fields such as the natural sciences are more likely to exhibit greater degrees of consensus, as indicated by reviewers' recommendations about publication of submitted manuscripts, than soft fields such as the social sciences (Cicchetti 1991; Hargens 1975, 1988).
Although little work has examined differences in citation patterns across fields, results from research on disciplinary differences in consensus suggest that the determinants of citations should differ between the natural and the social sciences. For this reason, in this dissertation I chose research areas located in disciplines described as exhibiting different levels of consensus so that I could examine whether scientists working in areas marked by different levels of disciplinary consensus have similar or different citation patterns.
37 As mentioned above, scholars tend to agree that two important indicators of disciplinary consensus are the level of referee agreement over the disposition of submitted manuscripts and the discipline's journal rejection rate (Cicchetti 1991; Hargens 1988;
Zuckerman and Merton 1971). Zuckerman and Merton argued that these two indicators reflect differences in the extent of agreement on standards of scholarship across disciplines since journals with high rejection rates receive manuscripts which, according to the editor and the referees, fail to measure up even to minimum standards of scholarship. According to them, this "...suggests that these fields of learning are not greatly institutionalized in the reasonably precise sense that editors and referees on the one side and would-be contributors on the other almost always share norms of what constitutes adequate scholarship" (Zuckerman and Merton 1971:472).
Astrophysics has long been considered a high-consensus field because it exhibits one of the lowest journal rejection rates (Cicchetti 1991; Zuckerman and Merton 1971).
In contrast, economics has been described as a low-consensus field based on its high journal rejection rates (Blank 1991; Zuckerman and Merton 1971).^ Furthermore, economics has been plagued with debates over its epistemological origin as well as the role of empirical data in the application of its theories. Specifically, economists spend much time disagreeing over whether economics is a science (Eichner 1983), and whether it should focus on sophisticated mathematical models to the exclusion of the manipulation of empirical data (Cassidy 1996; Leontief 1983; Morgan 1988). These
^As an illustration, Zuckerman and Merton (1971:471) report a rejection rate of 24 percent for astrophysics compared to 69 percent for economics.
38 findings suggest that the two disciplines I chose for this dissertation exhibit differences in their level of consensus and thus are well suited for a comparison of the determinants of citations across the natural and the social sciences.
Specifically, because of a lack of agreement upon the rules of scholarship (Storer
1967), and a lack of consensus over what constitutes good or significant work
(Zuckerman and Merton 1971,1972), scholars working in economics should rely more on
"who one is" than on "what one says" than scholars in astrophysics when attempting to gauge the quality of one's scientific contribution, in part because they are likely to use indicators of "who one is" such as eminence or prestige of employing or doctoral institution as a proxy for the quality of the work produced in the absence of better indicators (Whitley 1984). The use of the double-blind reviewing system only for the social sciences is a reflection of the concern that scientists in those disciplines might use factors not directly related to quality of the actual article. Thus, the "double-blind" system evolved as a way to limit, if not entirely eliminate,® the practice of relying upon characteristics of the author as opposed to those of the article in fields plagued with low consensus over what constitutes quality work. This disciplinary difference in consensus over the worth of contributions should be reflected by a greater importance given to functionally irrelevant characteristics of the cited author (e.g., Ph.D. prestige, employment prestige, sex) — net of the cognitive content of the article — in economics than in astrophysics.
®Many reviewers are still able to "guess" the author of a paper (see Blank 1991). As mentioned above, few studies have examined differences in citation practices across fields. The little research existing on this topic has tended to support the argument that research in high-consensus fields tends to be more cumulative than research in low- consensus fields. Line (1979, 1981) and Griffith and Small (1983) found that authors working in low-consensus fields cite older literature than authors working in high- consensus fields. Furthermore, Cozzens (1985) and Bazerman (1988) showed that scientists working in hard and soft fields tend to cite for different reasons. "Hard" scientists tend to refer to the specific knowledge claim of an article as well as to the specific techniques employed, while "soft" scientists tend to refer to the general theme of an article. Specifically, Bazerman showed that social scientists spend much time situating the literature, often going back to the "classics" in a given field, and showing how their research improves or builds upon them. In contrast, natural scientists rarely if ever go back to old literature, often starting directly where the most recent contributions
left off. These findings further suggest the existence of disciplinary differences in the
importance of literature aging.
Hypotheses about Disciplinary Differences
The literature just reviewed leads me to formulate two specific hypotheses
regarding expected differences in citation patterns in an astrophysics and an economics
research areas. The first hypothesis stems from the work on literature usage across fields
(Bazerman 1988; Griffith and Small 1983; Price 1965, 1970). The argument regarding
the cumulative nature of knowledge in the natural and social sciences suggests that the
40 time elapsed between potentially citing and potentially cited articles should have a statistically significant negative effect on the presence of citations in astrophysics, but no significant effect in economics.
The second hypothesis stems from the work on the relationship between consensus and the reward system of science across fields (Cole 1979; Hargens 1988;
Zuckerman and Merton 1971,1972). As a result of lower consensus over what constitutes quality work in the social sciences, once controlling for aspects of the cognitive content of the cited article (e.g., quality, extent to which it builds upon recent knowledge, subtopic, whether it is mostly theoretical), I expect functionally irrelevant characteristics of the cited author (e.g., Ph.D. prestige, employment prestige, eminence, percent of female authors) to have a statistically significant positive effect on the presence of a citation in economics, but no significant effect in astrophysics.
Summary
Although much has been written on why scientists cite, most -- if not all — of the analytical schemes on the use of citations fall within one of two perspectives. The normative accoimt suggests that citations are a means to acknowledge intellectual debt, and thus are largely motivated by the content of the cited articles. In contrast, the social constructivist account views citations as tools of persuasion used by scientists to convince readers of the validity of one's assertions. Proponents of this view argue that the position of the cited author in the stratification structure of his or her discipline is an important determinant of what scientists cite.
41 What these two positions lack is an explicit understanding of citations as embedded within both a citing and a cited document. Building upon the work of Small
(1978), I developed a theoretical argument for conceptualizing citations as dyadic relationships. Small argued that the meaning of the cited work is created by the citing author and therefore characteristics of both citing and cited articles should be included in models of the determinants of citations.
After formulating hypotheses regarding the expected effects of the variables included in the analysis I reviewed the literature that leads me to expect disciplinary differences in the determinants of citations. Much of the research on disciplinary differences has focused on the issues of consensus and the cumulative nature of research.
Past scholars have argued that because the hard sciences are more codified and have greater levels of consensus over what constitutes good work than the social sciences, scientists in those fields should rely more on "what one says" (indicated by cited article characteristics) than on "who one is" (indicated by cited authors characteristics) when using scientific contributions (Whitley 1984; Zuckerman and Merton 1971,1972).
Furthermore, as a result of the greater cumulative nature of knowledge in the natural sciences (Price 1965,1970) astrophysics articles should cite a more recent literature than economics articles.
42 CHAPTERS
DATA AND METHODS
The Research Areas
In order to compare whether the factors leading to a citation are the same in a hard and a soft field I selected two research areas: "celestial masers" an astrophysics area, and "rational expectations" an economics area. Focusing on the research area rather than the discipline is necessary to insure that all articles in the study are topically related and likely to cite one another. I chose these areas partly for convenience, — the data had already been collected by Hargens (1993) — and partly because 1 wanted to be able to compare a behavioral science and a physical science research area that were both fairly typical of research in their respective disciplines, were large enough to have generated a substantial amount of scientific contributions over a period of several years, and also differed in their levels of consensus as indicated by their disciplinary journal rejection rates as well as popular accounts of their development. In addition, I wanted areas that were fairly similar in terms of size (i.e., the number of papers they had produced) and time span (i.e., the decades in which they developed).
Both research areas were started in the 1960s and have experienced similar growth patterns, as Figure 3.1 shows. As can be seen from this figure, the growth of both
43 [See Figure 3.1] areas follows the shape of a logistic curve rather than that of a straight line.' In her study of invisible colleges, Crane (1972) argued that the rate of growth of publications in a research area can be explained as a social influence process. The exponential growth phase of scientific knowledge characterized by the logistic curve can be interpreted as a
"contagion" process in which early adopters influence later adopters as a result of intense communication with one another, which in turn creates an exponential increase in the number of publications. In contrast, "when individuals in a system are not in communication with one another, the probability that a member of the system will adopt an innovation remains constant and the pattem of growth is linear" (Crane 1972:23). In her study Crane showed that in two areas (rural sociology and mathematics of finite groups) where it was clear that interaction was high among the members, and awareness of each other's existence and mutual influence and communication occurred, the cumulative growth of publications followed the characteristic pattem of the logistic curve. In contrast, in two areas in which the level of interpersonal communication and influence was low (invariant theory and reading research), the cumulative growth of publications was linear.
The pattem exhibited by the literature growth in celestial masers and rational expectations suggests that both areas were characterized by heavy interaction
'I tested whether the growth of papers in these two areas was non-linear by regressing the cumulative number of papers on year and year squared. The squared term for number of years was positive and statistically significant in both areas, confirming that the relationship between growth of articles and year is non-linear.
44 450-,
400-
350-
300- Radonal Expectations •a a 250-
200- Celestial Masers ^ 150-
100-
50-
1 3 5 7 9 11 13 15 17 Literature Age
Figure 3.1. Literature Growth in Celestial Masers and Rational Expectations
45 among their members, a notion supported by popular accoimts of the development of these areas (see below).
The two research areas I chose for this dissertation were studied by Hargens
(1993) as part of a larger project examining reference network structure and development in a variety of research areas. Hargens used two main criteria in choosing these areas.
First, he chose areas whose participants generally believed they had made substantial progress — that is, they believed that their scholarly efforts produced a better understanding of their subject matter. Second, he chose areas whose literatures contain from 200 to 500 documents, the majority of which are journal articles. He imposed this restriction not only to facilitate data collection, but because researchers working on a topic they believe is "hot" prefer journal articles to monographs as publication outlets
(Garvey 1979).
Hargens (1993) identified the population of published documents in these areas
through (1 ) interviews with key informants in these areas — scholars who had participated
to the development of their field, (2 ) searching for literature reviews and examining indexes and on-line databases for keywords, and (3) an "iterative" process of collating references contained in core documents in each area and then examining frequently cited documents from the collated list to determine if they are part of the literature.
Specifically, Hargens identified 384 documents on celestial masers since the first paper published by Weaver, Williams, Dieter, and Lum in 1965, and 1980, the date at which he stopped his bibliography. In the area of rational expectations he identified 391 documents published between Muth's (1961) first paper on the topic and 1984, his
46 stopping date. In the next two sections I provide a brief description of the content and development of these two areas. The purpose of these descriptions is not to provide a comprehensive overview of each area but to give the unfamiliar reader basic background information on them.
Celestial Masers
Weaver et al. (1965) were the first to detect what would later be known as celestial masers. While studying the characteristics of the hydroxyl molecule (OH) in spatial regions of hot, ionized hydrogen (HIl regions), they found surprisingly intense emission. They called the emitting substance "mysterium" for a lack of a better term
(Elitzur 1995), and described it as the equivalent of an extremely bright microwave in space. It only took a few years for a team of researchers led by Moran (Moran, Burke,
Barrett, Rogers, Ball, Carter, and Cudaback 1968) to come to the conclusion that the
"mysterium" emission was not the sign of a new molecule but rather due to a maser radiation process. Only masers, they argued, could achieve such a high level of brighmess.- Early additional research established that astronomical maser emission occurred during both the early and late stages of stellar evolution, thus providing invaluable information on both. Elitzur (1995:68) described the process of maser formation in the following way:
^Earthly masers had been invented in 1953 by Charles H. Townes and were the precursors of lasers; the acronym stands for microwave amplification by stimulated emission of radiation.
47 Masers and lasers arise from a condition known as population inversion, in which the number of atoms or molecules in a higher-energy state exceeds that in a lower- energy state — the reverse of the normal state of affairs. As a result, the response of the inverted population to incoming photons also reverses conventional behavior. When atoms or molecules encounter photons of the appropriate wavelength, they generally move from a lower-energy state to a higher one. In inverted populations, however, most of the atoms or molecules are already in the higher-energy sate, and so they respond instead by emitting a photon. Each emission then triggers the release of additional photons, and the incoming light is amplified rather than being absorbed.
Since these early observations, radio astronomers have detected maser emissions from many different molecules and in a variety of astronomical sources. Cheung, Rank,
Townes, Thornton, and Welch (1969) identified water vapor (HiO) as a masing species.
HjO masers are considered to be the most powerful of the known sources of celestial
luminosity. Other molecular species that emit strong masers are methanol (CH3 OH), discovered by Barrett, Schwartz, and Waters in 1971, and silicon monoxide (SiO), discovered by Snyder and Buhl in 1974. Recently, a few additional molecules that exhibit weak maser emissions have been discovered (see Reid and Moran 1988 for a review). Furthermore, astrophysicists have observed maser activity in three main areas:
( 1 ) interstellar masers occur in regions where the dust and gas density are considerably
higher than those usually detected in giant molecular clouds, 2() stellar masers are associated with infrared (IR) stars, and (3) masers of extremely intense luminosity have been detected in other galaxies than our own.
The discovery of celestial masers was considered a major development in twentieth century astronomical research (Cook 1977; Elitzur 1995; Reid and Moran
1988). First, because astronomers cannot manipulate their objects of study, the only
48 information they can gather on celestial objects is from the radiation they emit. Maser radiation carries unique information about interstellar structures and stages of star formation and extinction. In addition, masers form only under certain conditions, and so astronomers can infer detailed profiles of pressure, temperature and gas velocity from their radiation. Finally, masers can be used as direct indicators of galactic and extragalactic distances via trigonometric and statistical parallax measurements.
Rational Expectations
The area of rational expectations developed in the 1970s as a reaction against
Keynesian macro-economic policy. Until Muth's (1961) pathbreaking paper on rational expectations, macroeconomic theory was based on the idea that the use of government fiscal and monetary tools could stabilize the ups and downs of the business cycle. As
Miller (1994;xiii) put it:
Before the revolution, macroeconomic policymaking was viewed as an engineering problem. Guiding the economy is similar to guiding a rocket ship - or so I was taught in the 1960s. A macroeconomic policymaker’s goal was to keep the economy on a full employment, noninflationary path, similar to an engineer’s goal of keeping a rocket ship on its course. The policymaker had policy tools to control, such as tax rates or base money, similar to an engineer’s levers and dials. The policymaking problem was posed as adjusting the tools based on new information about the economy’s position and the economic environment to best keep the economy on its full employment, noninflationary path.
This engineering paradigm was seriously questioned by Lucas' early papers
"Expectations and the Neutrality of Money" (1972) and "Econometric Policy Evaluation:
A Critique" (1976). Lucas' main argument was that engineering models devised to
49 capture the movement of inanimate objects were seriously flawed when used to explain economic human behavior. Specifically, Lucas argued that people would act in order to maximize their own interests in the face of changing government policies. Lucas' message was a simple one: individuals are rational actors who take future policy actions
into consideration and attempt to anticipate those actions by taking steps to counteract their effects.^ This argument was elaborated in a series of papers by Lucas and other economists who came to form the "rational expectations" school of macroeconomics.
Thus, the implication of the rational expectations hypothesis is that people learn to anticipate government fiscal and monetary policies to manipulate the economy and adjust their behavior accordingly, often canceling or reversing the desired effects. It is assumed
that individuals form expectations about the future that are based on all of the currently
available information, and that they use the best techniques available for drawing
inferences about the future implications of this information. Much of the later research
on rational expectations focused on developing empirical tests of this hypothesis.
The implication of rational expectations theory that the government's attempts at
influencing the economy in the long run are futile, because they will be anticipated by
knowledgeable actors, was seen as a major development in economic theory and had far-
reaching consequences on the application of economic policy (Attfield, Demery, and
^Some sociologists have used the same phenomenon to argue that a "science of society" is impossible since human beings constantly modify their behavior as a consequence of acquiring new knowledge (Nagel 1961:466-473; Winch 1958:91-94). Yet economists have developed an entire research program around modeling individuals' anticipated responses to new information.
50 Duck 1985; Miller 1994; Shaw 1984). Rational expectations theory was used by the government during the 1980s and early 1990s to justify a hands-off approach to the
American economy. The importance of rational expectations for economic theory was also acknowledged by the Swedish Royal Academy of Sciences when it gave its highly- coveted Nobel Prize in Economic Science to Lucas in 1995.
The Sample
I used stratified random sampling procedures to obtain a sample of 100 articles for each of the two fields in my analyses. I started from the entire population of published scientific contributions identified by Hargens (1993) for each of the two areas. My first step was to remove three books from Hargens' bibliographies because their focus was on reviewing the literature in their respective field rather than reporting original research.’*
Consequently, my sampling fimne was made up of the entire population of articles in each of the two areas — 383 articles in celestial masers and 389 articles in rational expectations.
In order to insure that my sample included a good representation of highly-cited and less-cited papers, as well as a good representation of papers over time I used a double-stratification method (see Moser and Kalton 1971; Sudman 1976). From the sampling frame for each area I first organized the papers in ten strata of forty papers each
‘‘There were two books in rational expectations and one in celestial masers.
51 by time of publication.* Within each stratum I then ordered the papers into another ten substrata by the total number of citations they received from all papers in the area from high to low. I then used a table of random numbers to select one paper from each of the four-paper substrata.* My application of this technique resulted in a sample of 100 articles for each area (or 26.1 percent for celestial masers and 25.7 percent for rational expectations). I then photocopied all the articles in my samples and used them as my main source of data for this research. Appendices A and B provide the list of the 100 articles in celestial masers and rational expectations respectively.
Network Structure of the Dataset
One of the main contributions of my research lies in its application of a network analytic approach to the study of citations that matches all the links between later papers and earlier papers and treats the potential citation link between two papers as the unit of
analysis. Thus my next step was to construct a data matrix matching every paper (that is
its article, author, and journal characteristics) with every other previously published paper
in an area. Since every Nth paper in an area has N-1 previously published papers that it
can cite, this resulted in a data matrix made up of (N^-N)/2 observations for each area (for
*Only the last stratum in each area had fewer than forty papers.
*Since the last stratum in both fields had fewer than forty papers, 1 used a table of random numbers to draw an additional three papers in celestial masers and two papers in
rational expectations from the list of unselected papers in order to get1 0 0 papers in each area.
52 N=100, this equals 4950)7 Consequently my dataset is made up of 4950 cases for each of the two research areas.
I modeled the likelihood of a citation occurring between two papers as a function
of seven sets of characteristics: ( 1 ) characteristics of the potentially citing article, (2 ) characteristics of the potentially citing article's author(s), (3) characteristics of the journal in which the potentially citing article is published, (4) characteristics of the potentially
cited article, (5) characteristics of the potentially cited article's author(s), (6 ) characteristics of the journal in which the potentiallycited article is published, and (7) relational variables indicating the presence or absence of various ties between potentially citing and potentially cited articles and their corresponding author(s) and journals.
Description of the Variables
In this section I give the operationalizations of the four sets of variables I
discussed in chapter2 : article characteristics, author characteristics, journal characteristics, and relational variables. Since in each area every potentially citing paper is also a potentially cited paper — with the exception of the first and last papers — the description of the article, author, and journal variables applies to both the potentially citing and the potentially cited papers.
’It is possible that an earlier paper cites a later paper that is forthcoming. In my data, there were two citations to forthcoming papers in rational expectations (out of 147 citations), and six in celestial masers (out of 190 citations). Of those eight citations, six were self-citations. Because of their nature (self-citations) and their small number, 1 did not include citations to forthcoming work in my analyses.
53 The Dependent Variable
Since I examined only potential citations to previously published papers, the dependent variable is whether a citation occurs between a later paper and an earlier paper in a given research area. Thus, with k representing a paper's position within all the papers in a research area, every paper has k-1 citation possibilities (for example, the 46th paper has the possibility to cite the previous 45 papers).Citation occurrence from a later paper
to an earlier paper is coded1 for yes, 0 for no.
Characteristics of the Article
These variables attempt to capture both the content of an article and other
available characteristics that could possibly have an influence on citing decisions. 1 indicated the total number o f authors by the raw count of individuals listed as having contributed to the article. Book chapter is coded 1 if the article was published in an
edited book, 0 if the article was published in a refereed journal.*
The total number of references is a raw count of the number of scholarly works the article refers to. This includes references to journal articles, edited chapters, books, conference proceedings, work in progress, preprints, and any other work an article listed explicitly either in a bibliographic references section or in a footnote at the bottom of a page. Theproportion o f recent references indicates the extent to which an article cites
*I also coded the language in which the article was written. However, because in my celestial masers sample all articles were written in English, and in the rational expectations sample only two articles were written in a foreign language,1 decided not to include this variable in the analyses.
54 recent knowledge and is measured by the number of references to scholarly works published within the three years prior to an article's publication year divided by the total number of references in an article (e.g., if an article is published in 1980, this counts all references to articles published in 1977,1978, 1979, and 1980). Another indicator of knowledge recency is the proportion o f references to forthcoming measured work, by the number of references to work yet unpublished but already accepted for publication divided by the total number of references in an article.
The proportion o f top journal references reflects the extent to which an article relies upon highly visible contributions in developing its argument, as indicated by the number of references to articles published in the top five journals in the field divided by the total number of references in the article.® I determined which where the top five journals in each field by the mean 1975-1979 Journal Citation Report (JCR) impact factor for celestial masers {Science Citation Index [SCI] 1976-1980) and the mean 1977-1981
JCR impact factor for rational expectations{Social Science Citation Index [SSCI] 1978-
1982).'° The JCR impact factor is computed by dividing the total number of citations a journal receives within a two-year period by the total number of articles it published
®This indicates the top five journals among the distribution of all journals in my samples. Thus the top five journals in my samples of celestial masers and rational expectations articles may not be the same as the top five journals for the fields of astrophysics or economics as a whole.
'“Although I would have liked to have information for the same time periods to compute the average JCR impact factor for both fields (i.e., 1975-1979), the Institute for Scientific Information did not start including an impact factor for the social sciences until 1977.
55 within this same period, and is commonly used as a measure of journal quality or visibility (Allen 1990; Bott and Hargens 1991). Consequently, the top five journals for the field of celestial masers were: (1) Physical Review Letters, (2) Science, (3) Nature,
(4) Astrophysical Journal (including Astrophysical Journal Letters since they were not published separately between 1975 and 1979), and (5) Astrophysical Journal
Supplement}^ The top five journals for rational expectations were: i\) Journal of
Economic Literature, (2) Brookings Papers on Economic Activity, (3) Journal o f Political
Economy, (4) American Economic Review, and (5) Econometrica}^
I included a variable indicating the number o f pages an article had. I also included variables indicating the number o f equations per page, number o f quantitative tables and graphs per page, and the number o f theoretical figures per page. These three variables are based on all equations, tables, graphs, and figures appearing anywhere in the article (i.e., whether in the main text, in a footnote, or in a appendix) and are computed by dividing their total count by the total number of pages in the article.
After carefully reviewing each article's content, 1 indicated the theoretical purpose
of the article with a dichotomous variable coded 1 if the article claimed to develop a new
Annual Review o f Astronomy and Astrophysics had the highest impact factor in the field, but because it is a review serial published only once a year 1 did not include it in the list of top journals.
Journal o f Economic Literature publishes mostly reviews. However, it also publishes some non-review articles that tend to be overviews or syntheses of research areas. For example, one of the articles in my sample that it published, "A Child's Guide to Rational Expectations" (Maddock and Carter 1982), is a brief overview (13 pages) of the rational expectations research program and has only 35 references. For this reason, 1 treated Journal o f Economic Literature as a regular journal.
56 theoretical argument, otherwise 0. I also measured whether the article was aresearch note, a review o f the literature, or mostly critical of the literature in its area with three
dichotomous variables coded 1 for yes, 0 for no.
Much of the debate over the use and functions of citations has revolved around the issue of article "quality" (see Cole 1992; Cole and Cole 1973; Kaplan 1965; Latour 1987;
Zuckerman and Merton 1971). Largely as a result of their study design, past studies of citations have not been able to assess the role of an article's quality upon its subsequent citation.'* Here I measured the quality o f the article by the count of the number of times an article was subsequently cited in years 3 and 4 after publication, excluding self citations and, for a given tie, excluding the citation by the citing paper.A lthough some argue against using citation counts as an indicator of quality, there is much consensus that it is the best available measure of a paper's quality (Bayer and Folger 1966; Clark 1957;
I judged an article critical if it contained direct negative comments on another work in the area or on the overall development of the research area. In general, the author's position was unequivocal as the following quotes indicate: "The purpose of this paper is to argue that there is a serious weakness in the tests carried out by Pesando and Carlson" (Mullineaux 1978:329); or "...I regard Barro's contracts [theory] as being irrelevant to the issue at hand....if a theory implies results that are remote from reality...then our conclusion should be that something is missing from the model, rather than that the real world behaves as the theory implies" (Fischer 1977:321); or again "At this point the validity of the more critical assumptions made in the above calculation [by Mezger et al. 1967] must be examined carefully" (Holtz 1968:L119).
‘*This requires a study design where the unit of analysis is the citation itself. See my earlier elaboration of this argument in chapter 2 .
'*! excluded the citations coming from the citing articles in my sample from these counts to avoid having an independent variable be partly a function of the dependent variable.
57 Cole and Cole 1973). Citations indicate the extent to which a paper is subsequently used by other scientists, and for this reason it is unlikely that highly-used papers are, on average, of little worth.
Characteristics of the Article's Authorfs)
In this set of variables I attempted to capture the author's demographic characteristics, educational background, place of employment, and scientific eminence that could have an effect on the decision to cite.
I included a variable indicating the percentage o f 'women among the article's authors computed by dividing the number of women authors by the number of total authors and multiplying by one hundred. I used first names to determine the authors' sex.
In the case of gender neutral, ambiguous, or foreign names, I coded the information as missing.'* I also included a measure of thepercentage o f authors employed by a university by dividing the number of authors who listed a university affiliation on the article by the total number of authors and multiplying by one hundred.
I obtained information on the authors' Ph.D. institution, year of Ph.D., and rank at time of the article's publication firom several biographical sources. For economists I used the American Economic Association (AEA) Directories o f Members (AHA 1974, 1981,
1985, 1989, 1993), and American Men and Women o f Science for Economics (1974). For astrophysicists I used theDirectory o f Physics and Astronomy Faculties in North
'*The valid number of cases for all the variables on which I had missing data is reported in Tables 3.2 through 3.5.
58 American Colleges and Universities (1970), the International Physics and Astronomy
Directory (1969), the Directory o f Physics and Astronomy Staff Members ( 1976), the
American Astronomical Society (AAS) Membership Directories (AAS 1995,1996), and
American Men and Women o f Science (1989). In addition, I contacted scientists for whom I lacked information and who were professionally active via electronic mail. I used this information to create the authors' variables discussed below.
Because of the centrality of the concept of prestige in social constructivist accounts of the allocation of rewards in science, ideally I would have wanted similar measures of Ph.D. and employment prestige for both areas. However, as a result of the lack of prestige ratings of astrophysics departments until 1993 (i.e., Goldberger, Maher, and Flattau 1995), I used two alternate measures. I measured theprestige o f the author's
Ph.D. institution for celestial masers by the number of papers published by his or her
Ph.D. institution in Astrophysical Journal, Astronomical Journal, and Publications o f the
Astronomical Society o f the Pacific in 1982 as reported by Abt (1993).'^ I gave foreign institutions and U.S. institutions not listed in Abt the midpoint score of the possible range.'* For rational expectations, I measured theprestige o f the author’s Ph.D.
’’The zero-order correlation between the Goldberger et al. (1995) and Abt (1993) ratings is r=.49. This moderate correlation can partly be explained by the fact that many institutions listed in Abt are non-academic research institutes not included in Goldberger et al. For my sample, in which I have a number of scientists working in such non- academic settings, the Abt ratings and better suited.
'*Since most individuals received their Ph.D.s from a handful of highly prestigious schools in the U.S. that were also the top article producing institutions (e.g., CIT, Harvard, Berkeley, Chicago), the sample mean was very high. However, most (continued...)
59 institution by the 1982 National Research Council (NRG) score for the "quality of program faculty" for economics (Jones, Lindzey, and Coggeshall 1982). This score ranged from 0 to 5, with 5 indicating "distinguished," and 0 "not sufhcient for graduate education." For the reasons mentioned in footnote 17,1 gave foreign and non-academic institutions the midpoint score of the possible range. Since some articles (especially in celestial masers) have more than one author, and hence more than one prestige score, I included three different measures of Ph.D. prestige: (1) the highest score among the authors, (2) the mean score for all the authors, and (3) the median score for all the authors.'® Furthermore, in order to address the issue of the non-comparability of this measure across areas, I performed a sensitivity analysis by re-estimating the effects of
Ph.D. and employment prestige using the 1993 NRG ratings of the quality of the program faculty for astrophysics department (i.e., Goldberger et al. 1995). The 1993 rating uses the same scale as the 1982 rating for economics (see above) and thus is the most comparable measure available. However, because they only include prestige ratings for
academic institutions and many astrophysicists in my sample work in non-academic
settings, they generate much missing data for the employment prestige variable.
'^(...continued) foreign-trained scholars came from second tier institutions (e.g., Groeningen, Onsala, Nuffield, Sussex, Tasmania). Similarly, if a U.S. institution was not listed by Abt (1993), it is because it had a negligible productivity (e.g., Indiana, Michigan State, Iowa). Thus giving foreign or non-ranked U.S. institutions the sample mean would have overrated them.
'®In the rare case where an individual did not have a Ph.D., I coded the information on Ph.D. prestige and years elapsed since PhD. as missing.
60 I measuredyear o f Ph.D. by subtracting the year the author received the doctorate degree from the current year (1996) so that the interpretation of Ph.D. year would be more intuitive (i.e., so that the longer it has been since one graduated, the higher the value for Ph.D. year). Again, because of the issue of multiple authorship, I included three different measures: the highest score, the mean, and the median. I indicated the rank of the author(s) by the percentage o f authors who are jiill professors. For authors in non- academic settings, I counted directors of research centers or program directors as full professors.
I used the affiliation an author listed on the article as his or her current place of employment at the time of the article's publication. From this information I measured the prestige o f the author's employing institution by the 1982 Abt scores (1993) for celestial masers, and by the 1982 scores for the "quality of program faculty" for economics (Jones et al. 1982) for rational expectations. These scores are the same as the ones used to measure Ph.D. prestige (see above).^°
I measured the author's eminence by the total number of citations — excluding self-citations — the author received in the year prior to the article's publication year. I obtained citation counts for astrophysicists from the Science Citation Index (1965-1979)
Although there are earlier prestige ratings of economic departments that might correspond more closely to the time at which scientists in my sample received their Ph.D. (Cartter 1966; Roose and Andersen 1970), they contain ratings on many fewer departments than the 1982 ratings, largely because many graduate programs were created in the 1970s. Since most prestige or reputational rankings are highly stable over time, with correlations ranging from .92 to .97 (Baldi 1994; Keith and Babchuk 1994; Jones et al. 1982; Goldberger et al. 1995), I decided to use the same years as indicators of Ph.D. prestige and current employment prestige.
61 and for economists from the Social Science Citation Index (1969-1983). Again, in cases
of multiple authorship, I included the highest, the mean and the median as measures of
eminence. Finally, I indicated the number of previous articles the author published in the area by counting all of the author's previous publications as either first or junior author in the area, based on the list of the entire population of papers in either celestial masers of
rational expectations. Multiple authorship was also handled by including the highest, the
mean, and the median scores.
Journal Characteristics
These variables capture the journal characteristics that could influence whether a
citation occurs among two papers. The information about circulation and ownership
came from the following sources: Ulrich's International Periodicals Directory (1980),
Irregular Serials and Annual (1982), Cabell's Directory o f Publishing Opportunities in
Business and Economics (Cabell 1988), and Gale Directory o f Publications (1988).
I measuredpublication ownership by a dichotomous variable coded 1 if the journal is published by a professional association (e.g., American Economic Association,
American Astronomical Society), otherwise 0. I followed the same coding scheme for
articles in edited volume. If the edited volume was published by a professional
association, it received a 1, otherwise 0. Journal circulation indicates a journal's yearly
62 number of subscribers. For articles that appeared in edited volumes, I computed their circulation based on their estimated JCR impact factor (see below).^’
Finally, I measuredjournal quality by the mean 1975-1979 JCR impact factor (ISI
1976-1980) for celestial masers, and the mean 1977-1981 JCR impact factor (ISI 1978-
1982) for rational expectations.^ I gave journals that did not have a JCR impact factor a score of zero on the assumption that they are almost never cited.^ In addition, for edited volumes which are not included in the JCR, I computed the equivalent of an impact factor by using the following formula:
IF = TC /(TA *2), where IF is the estimated impact factor; TC is the total number of citations received by all the articles published in the edited volume in the two years following its year of publication; and TA is the total number of articles the edited volume published. Since the articles in an edited volume are only published once for the two year period, I multiplied
TA by two in order to have a denominator comparable to the actual JCR impact factor.^'*
^'I used 1979-1980 as the year for which I obtained circulation figures. However, for a few journals not listed in Ulrich's (1980), I used circulation figures for 1988 from Cabell's (Cabell 1988) and Gale (1988).
^ See footnote 10.
“ ISI includes most journals which receive some citations. For example, for economics it includes the Rivista Internazionale di Scienze Economiche e Commerciali with a mean 1977-1981 impact factor of .006. Thus it is reasonable to give journals not included by ISI a score of zero.
“ See my earlier description of how the JCR impact factor is computed on pages 55-56.
63 Relational Variables
My network approach to citation flows allows me to include a set of relational variables that indicate various relationships between the potentially citing and the potentially cited articles and their corresponding authors and journals. Whether the potentially citing and the potentially cited articles were published in the same journal, both mostly theoretical, and studying the same subtopic, are all dichotomous variables coded 1 for yes, 0 for no. I determined the subtopics through keywords in the abstract as well as careful examination of the text of the articles. For rational expectations I
distinguished the following five subtopics: (1 ) interest/exchange rates and trade, ( 2 ) methods, (3) unemployment, (4) prices/inflation/business cycle/monetary or fiscal policy, and (5) overview of the field but not a review.^ A single dichotomous variable indicates whether the potentially citing and potentially cited articles have any of these five subtopics in conunon.
For celestial masers I distinguished two central subtopics: (1) whether the potentially citing and potentially cited articles study the same masing molecule (i.e., OH,
H2 O, SiO, CH3 OH, other/don't know), and (2) whether they study the same type of maser
(i.e., interstellar, stellar, extragalactic, other/don't know). A single dichotomous variable indicates whether the potentially citing and potentially cited articles either study the same molecule or the same galaxy.
-^Overviews tend to provide brief summaries of key perspectives or developments in the area while reviews are extensive and detailed accounts of the entire work produced in the area up to that point.
64 I also included relational variables indicating whether any of the potentially citing and potentially cited articles' authors are thesame authors, ever worked at the same institution, or received the Ph.D. from the same graduate department. These are
dichotomous variables coded 1 for yes, otherwise 0 .
Finally, I included a measure o f years elapsed between the potentially citing and the potentially cited articles by subtracting the year in which the potentially cited article was published from the year in which the potentially citing article appeared.
Table 3.1 lists all the variables included in my analyses and summarizes their operationalizations.
[See Table 3.1]
Missing Data
Because much of the author information for this research comes from secondary biographical sources, I was limited by the availability of such sources. In particular, the dearth of biographical sources on scientists working outside the United States made the collection of data on foreign scientists somewhat difficult. While I have no missing data on the article characteristics since they are all coded from the articles themselves, I had some missing information on scientists' background characteristics (Ph.D. institution, year of Ph.D., sex, rank, and sector of employment), as well as journal circulation figures.
Most methods of handling missing data operate on the assumption that the missing data are random and thus not a function of particular characteristics of a variable
(i.e., missingness on X, does not depend on values of X,), or not a function of other
65 Variable Operationalization
Dependent Variable
Citation Occurrence Citation occurs between a later and an earlier paper. l=yes; 0=no.
Article Characteristics'
Number of Authors Sum of number of authors.
Book Chapter l=article in edited book; 0=refereed journal article.
Number of References Sum of number of references.
Proportion of Recent References Number of references to work published within previous three years over total number of references.
Proportion of Top Journal References Number of references to top five journals in the field over total number of references.
Proportion of References to Sum of references to papers to be published over total Forthcoming Work number of references.
Number of Pages Sum of number of pages.
Equations per Page Sum of number of equations over number of pages.
Quantitative Tables and Graphs Sum of number of quantitative tables and graphs over per Page number of pages.
Theoretical Figures Sum of number of theoretical figures over number of per Page pages.
Theoretical Purpose I=article claims to make a theoretical contribution; 0=else.
Review Article l=article is a review of literature; 0=else.
Critical Article l=article is critical of research in its field; 0=else.
(Continued on next page)
Table 3.1. Variables List and Operationalizations
66 Table 3.1. (Continued)
Variable Operationalization
Article Characteristics (Continued)'
Article Quality Citation counts in years 3 and 4 after publication (from Science Citation Index and Social Science Citation Index), excluding self-citations and citations coming from papers in my sample.
Author Characteristics'
% Female Authors Percent o f authors who are women.
% Authors in Universities Percent o f authors who work in university settings.
Ph.D. Prestige Prestige of authors' Ph.D. institution. Scores are from Jones et al. (1982) for economists, and Abt (1993) for astrophysicists.
Year of Ph.D. Measured by subtracting the author's Ph.D. year from (reverse coded) the current year (1996).
% Full Professors Percent of authors who are full professors.
Employment Prestige Prestige of authors' employing institution. Scores are from Jones et al. (1982) for economists, and Abt (1993) for astrophysicists.
Eminence Total number of citations received by the author in the year prior to the article's publication year. Citation data are from the Science Citation Index for astrophysicists, and the Social Science Citation Index for economists.
Previous Articles in Area Number of the author's previous publications as either first or junior author in the area, based on population of published papers in each area.
Journal Characteristics'
Publication Ownership l=Joumal is published by a professional association; 0=else.
(Continued on next page)
67 Table 3.1. (Continued)
Variable Operationalization
Journal Characteristics rContinuedV
Journal Circulation Yearly number of subscribers.
Journal Quality Measured by the mean 1975-1979 Journal Report (JCR) impact factor (ISI 1976-1980) for celestial masers, and the mean 1977-1981 JCR impact factor (ISI 1978-1982) for rational expectations.
Relational Variables
Same Journal l=potentially citing and cited articles are published in same journal; 0=else.
Both Theoretical I=potentially citing and cited articles are both mostly theoretical; 0=else.
Same Subtopic I=potentially citing and cited articles study the same subtopics; 0=else.
Same Authors I=any of the potentially citing and cited authors are the same individual(s); 0=else.
Work Colleagues l=any of the potentially citing and cited authors have ever worked at the same institution; 0=else.
Same Ph.D. Institution l=any of the potentially citing and cited authors received their Ph.D. from the same graduate institution; 0=else.
Years Elapsed Between Articles Computed by subtracting the cited article publication year from the citing article publication year.
'These variables are for both the potentially citing and the potentially cited articles.
68 variables (i.e., missingness on X, does not depend upon values ofX^; Little and Rubin
1990). However, this is not the case in my data. A careful examination revealed that most missing author data were related to the author being from a foreign country. This suggests that using ways to handle missing data that depend upon the assumption of randomness would be inappropriate and result in biased estimates in most cases.
The strong suspicion that the missing data on most variables were not missing randomly led me to handle missing data in several ways. In a first stage, in order to compute a score that involved multiple authors (e.g., Ph.D. prestige, percent of authors in university setting, etc...), I computed that score based on the number of authors for which
I had available information as long as I had information on at least half of the authors.
For example, if six individuals co-authored an article and I had Ph.D. prestige information on only five of them, I based the high, mean, and mode scores only on those five values. However, if I only had information on two of these authors, I coded the
Ph.D. prestige information as missing. This method limited the problem of missing data by using all the available information.
I then used one of two methods in order to handle the remaining missing data.
For all the variables for which I had reasons to believe the data were not missing randomly,^® I used mean substitution, using the means of the variables based on the 100 articles for each field, and including a dummy variable indicator of whether a given value was mean-substituted (see Cohen and Cohen 1975). For journal circulation, I used
“ This includes Ph.D. prestige, year of Ph.D., percent of authors in university setting, rank, and sex.
69 simple imputation techniques (see Little and Rubin 1990) where I regressed journal circulation on journal qualityB ecause in masers three journals had extremely high circulation rates that were far above the mean for the area (Science, Physics Today, and
Nature), I excluded them from the regression to avoid inflating the estimates of journal quality. Consequently, I estimated the missing values for journal circulation using the following equations:
For rational expectations,
Y (circulation) = 1880.665 + 5533.317 (journal quality).
For celestial masers,
Y (circulation) = 791.309 + 852.700 (journal quality).
As a background for the following analyses. Tables 3.2 and 3.3 report the means, standard deviations, high and low values for the article, author, and journal characteristics
in the sample of1 0 0 articles for both celestial masers and rational expectations, respectively. Tables 3.4 and 3.5 report the means, standard deviations, high and low values for the dependent and relational variables in the sample o f4950 paired potentially citing/cited papers for both the astrophysics and the economics area.
[See Tables 3.2 through 3.5]
” The zero-order correlation between journal quality and journal circulation is .72 in celestial masers, and .61 in rational expectations.
70 Variable Mean S.D. Min. Max. Valid N
Number of Authors 3.20 2.54 1.00 15.00 100 Book Chapter .05 .26 .00 2.00 100 Number of References 25.08 33.25 .00 315.00 100 Prop. Recent References .51 .24 .00 1.00 100 Prop. Top Journal References .54 .18 .00 1.00 100 Prop. Forthcoming References .05 .06 .00 .24 100 Number of Pages 9.60 10.92 1.00 83.00 100 Number of Equations/Page .93 1.67 .00 7.50 100 Quantitative Tables/Page .59 .42 .00 2.00 100 TTieoretical Figures/Page .06 .14 .00 .70 100 Theoretical Purpose .32 .47 .00 1.00 100 Review Article .01 .10 .00 1.00 100 Critical Article .04 .20 .00 1.00 100 Article Quality 6.34 6.67 .00 32.00 100 % Female Authors 2.30 12.65 .00 100.00 93 % Authors in Universities 76.33 36.22 .00 100.00 99 Author's Ph D. Prestige 47.04 17.44 15.00 71.40 88 Author's Year of Ph D. 34.00 8.50 21.00 58.00 88 % Full Professors 36.26 34.35 .00 100.00 87 Author's Employment Prestige 44.95 16.05 15.00 71.40 100 Author's Eminence 39.72 35.42 .00 182.00 100 Author's Previous Area Articles 5.93 7.25 .00 36.00 100 Publication Ownership .73 .45 .00 1.00 100 Journal Circulation 5099.90 17106.63 500.00 160000.00 100 Journal Quality 3.23 1.62 .00 8.66 100
Table 3.2. Descriptive Statistics for Variables in 100 Articles in Celestial Masers
71 Variable Mean S.D. Min. Max. Valid N
Number of Authors 1.31 .58 1.00 4.00 100 Book Chapter .04 .20 .00 1.00 100 Number of References 19.33 14.22 .00 97.00 100 Prop. Recent References .29 .22 .00 1.00 100 Prop. Top Journal References .33 .18 .00 1.00 100 Prop. Forthcoming References .02 .05 .00 .29 100 Number of Pages 14.51 8.21 2.00 38.00 100 Number of Equations/Page 1.72 1.44 .00 6.17 100 Quantitative Tables/Page .08 .14 .00 .63 100 Theoretical Figures/Page .03 .08 .00 .44 100 Theoretical Purpose .34 .48 .00 1.00 100 Review Article .02 .14 .00 1.00 100 Critical Article .19 .39 .00 1.00 100 Article Quality 6.75 10.88 .00 60.00 100 % Female Authors 3.67 17.66 .00 100.00 100 % Authors in Universities 84.92 35.03 .00 100.00 100 Author’s Ph.D. Prestige 4.10 1.07 1.00 5.00 90 Author's Year of Ph D. 24.65 8.20 12.00 67.00 89 % Full Professors 51.11 47.98 .00 100.00 90 Author's Employment Prestige 3.29 1.01 1.20 5.00 100 Author's Eminence 25.72 46.17 .00 204.00 100 Author's Previous Area Articles 1.14 1.87 .00 8.00 100 Publication Ownership .36 .48 .00 1.00 100 Journal Circulation 6325.59 6829.34 900.00 27000.00 100 Journal Quality .86 .71 .00 3.66 100
Table 3.3. Descriptive Statistics for Variables in 100 Articles in Rational Expectations
72 Variable Mean SJ). Min. Max. Valid N
Citation Occurrence .04 .19 .00 1.00 4950 Same Journal .29 .45 .00 1.00 4950 Both Theoretical .56 .50 .00 1.00 4950 Same Subtopic .75 .43 .00 1.00 4950 Same Authors .05 .22 .00 1.00 4950 Work Colleagues .12 .32 .00 1.00 4950 Same Ph.D. Institution .20 .40 .00 1.00 4950 Years Elapsed Between Articles 4.54 3.27 .00 14.00 4950
Table 3.4. Descriptive Statistics for Dependent and Relational Variables for Celestial Masers
Variable Mean S.D. Min. Max. Valid N
Citation Occurrence .03 .17 .00 1.00 4950 Same Journal .03 .17 .00 1.00 4950 Both Theoretical .55 .50 .00 1.00 4950 Same Subtopic .90 .30 .00 1.00 4950 Same Authors .01 .08 .00 1.00 4950 Work Colleagues .02 .13 .00 1.00 4950 Same Ph.D. Institution .07 .25 .00 1.00 4950 Years Elapsed Between Articles 3.13 2.82 .00 14.00 4950
Table 3.5. Descriptive Statistics for Dependent and Relational Variables for Rational Expectations
73 Exploratory Analyses
Because of the large number of variables I collected, it is likely that some of them are redundant (i.e., measuring the same or closely related concepts). For example, the number of pages and the number of references in an article can both be seen as measures of an article's length. Thus, including both of these variables in a regression equation may result in their pardalling out their effects. As Gordon (1968) noted, when redundant independent variables are introduced together in a regression equation, they "partial out" each other's covariation with the dependent variable, leading to the often incorrect conclusion that they have no impact on the dependent variable. This is explained by the fact that their common predictive value gets averaged, in a weighted manner, over all of their regression coefficients. As a result, all of their regression coefficients decline in absolute value.
To address the issue of potential redundancy among my independent variables, I conducted a series of exploratory factor analyses for each of the four subsets of variables included in the study (i.e., article variables, author variables, journal variables, and relational variables).^* I used a principal component extraction method that retained
^®Except for the relational variables, I carried out the factor analysis on the sample of 100 articles. This was necessary to avoid distorting the results since in the network of paired potentially citing/cited papers, observations for articles, authors, and journals appear more than once. However, for the relational variables, which are specific to each pair, I conducted the factor analysis on the network of 4950 pairs of potentially citing/cited articles.
74 factors with eigenvalues over 1, and used "varimax" rotation.^’ Table 3.6 through 3.13 present the rotated factor matrix results for each subset of variables for both areas.
[See Tables 3.6 through 3.13]
Because my goal is to be able to compare the two areas by having similar variables in the scales used in both, I used information from the factor analyses
performed on both areas to inform the choice of variables to be included in the
construction of scales. This means that although in one area more variables might load
on a given factor than in the other, I computed the factors only based on the common
variables that loaded on each factor across the two areas.
The exploratory factor analyses identified eight factors common to both areas.
Among the article characteristics the analyses identified an article size factor made up of
the total number of references and the total number of pages, a recency o f knowledge
factor made up of the proportion of recent references and the proportion of forthcoming
references, and a theoretical content factor made up of the number of theoretical figures
per page, and whether the article claims to make a theoretical contribution. Among the
author characteristics the analyses identified an institutional prestige factor made up of
the author's Ph.D. prestige and employment prestige, anauthor eminence factor made up
of the author's eminence (as indicated by citation counts) and the number of articles
previously published in the area, and aseniority factor made up of the years of Ph.D. and
the percentage of authors who are full professors. Among the journal characteristics the
” For a discussion of these techniques, see Nunnally and Bernstein (1994).
75 Variable Factor 1 Factor 2 Factor 3 Factor 4 Factor 5
Number of Authors .298 -.160 -.511 -.174 .179 Book Chapter .080 .064 .095 .931 .069 Number of References .931 -.075 -.050 .040 -.062 Prop. Forthcoming References -.003 .868 -.160 .050 -.186 Prop. Recent References -.145 .714 -.061 .135 .387 Prop. Top Journal References -.046 .019 -.042 -.012 .945 Number of Pages .924 .003 -.028 .161 -.014 Equations/Page -.067 -.161 .685 -.073 -.234 Quantitative Tables/Page .007 .087 -.737 -.096 -.057 Theoretical Figures/Page .056 -.075 .612 -.102 .113 Theoretical Purpose -.054 -.005 .842 .225 -.041 Review Article .101 .028 -.001 .896 -.063 Critical Article .046 .400 .478 -.185 .018 'Results based on "varimax" rotation.
Table 3.6. Exploratory Factor Analysis of Article Characteristics for Celestial Masers
Variable Factor 1 Factor 2 Factor 3 Factor 4 Factor 5
Number of Authors -.146 .049 -.042 -.038 .519 Book Chapter .080 .564 .221 .020 -.295 Number of References .885 .052 .046 .001 -.142 Prop. Forthcoming References .050 .789 -.073 -.199 -.135 Prop. Recent References -.070 .710 -.095 .141 .210 Prop. Top Journal References -.020 .132 .758 -.114 -.064 Number of Pages .773 .055 .247 -.168 -.001 Equations/Page -.013 -.199 -.005 .132 .829 Quantitative Tables/Page -.060 -.064 -.139 -.845 -.160 Theoretical Figures/Page .066 -.087 .617 .215 .013 Theoretical Purpose .041 -.262 .573 .494 -.183 Review Article .734 -.059 -.133 .163 -.090 Critical Article -.289 -.106 .488 .509 -.368 'Results based on "varimax" rotation.
Table 3.7. Exploratoir Factor Analysis of Article Characteristics for Rational Expectations
76 Variable Factor 1 Factor 2 Factor3
% Female Authors -.238 -.025 -.539 % Authors in Universities -.032 -.780 .197 Author's Ph.D. Prestige .857 .007 -.026 Author's Years Since Ph.D. .023 -.176 .770 % Full Professors -.181 -.059 .794
Author's Bnployment Prestige .801 .267 - . 0 1 2 Author's Eminence -.180 .321 .469
Author's Previous Area Articles .282 .697 . 1 1 0 ^Results based on "varimax" rotation.
Table 3.8. Exploratory Factor Analysis of Author Characteristics for Celestial Masers
Variable Factor 1 Factor 2 Factor3
% Female Authors -.337 .406 -.620
% Authors in Universities .511 . 1 0 0 .128
Author's Ph.D. Prestige .817 .040 - . 0 1 2 Author's Years Since Ph.D. .045 .209 .757 % Full Professors .006 .378 .761 Author's Employment Prestige .791 .300 .070 Author's Eminence .313 .753 .313
Author's Previous Area Articles .188 .822 . 1 2 1 ^Results based on "varimax" rotation.
Table 3.9. Exploratory Factor Analysis of Author Characteristics for Rational Expectations^
77 Variable Factor 1
Publication Ownership .831 Journal Circulation .883 Journal Quality .288
Table 3.10. Exploratory Factor Analysis of Journal Characteristics for Celestial Masers
Variable Factor 1
Publication Ownership .647 Journal Circulation .796 Journal Quality .898
Table 3.11. Exploratory Factor Analysis of Journal Characteristics for Rational Expectations
78 Variable Factor 1 Factor 2
Same Journal Publication .313 .398 Both Theoretical .344 .132 Same Subfield -.072 .599 Same Authors .785 -.053 Work Colleagues .779 -.075 Same Ph.D. Institution .774 .081 Years Elapsed Between Articles -.032 -.711 Results based on "varimax" rotation.
Table 3.12. Exploratory Factor Analysis of Relational Variables for Celestial Masers'
Variable Factor 1 Factor 2 Factor 3
Same Journal Publication .072 .263 -.542 Both Theoretical -.004 .678 -.116 Same Subfield .031 .737 .076
Same Authors .849 -.024 - . 1 1 2 Work Colleagues .830 -.034 -.074 Same Ph.D. Institution .604 .093 .139 Years Elapsed Between Articles .060 .182 .824 Results based on "varimax" rotation.
Table 3.13. Exploratory Factor Analysis of Relational Variables for Rational Expectations'
79 analyses identified a journal quality factor made up of journal quality, journal circulation, and publication ownership.^® Finally, among the relational variables the analyses revealed the existence of a social ties factor made up of whether the authors of the potentially citing and the potentially cited articles obtained their Ph.D. from the same graduate school, and whether the potentially citing and potentially cited authors ever worked at the same institution/'
From the factors just identified I computed eight scales by summing up their z- scores in their respective dataset^^ and dividing by the number of items in the scale. I then conducted reliability analyses on these eight scales by computing Cronbach's alpha in both areas. Finally, I imported the scale scores computed on the 100 articles dataset to the full 4950 paired articles dataset. Table 3.14 and 3.15 report the means, standard deviations, high and low values and Cronbach's alpha for the eight scales in both the celestial masers and the rational expectations area.
[See Tables 3.14 and 3.15]
In a next step I conducted collinearity diagnostics following the procedures described by Studenmund (1992:274-276). These diagnostics identified no problems
^°By definition, only one factor can be extracted with three variables.
^'Although the variable indicating whether the potentially citing and potentially cited articles were written by the same author(s) also loads high on this factor in both areas, it does not make much theoretical sense to include it in a scale reflecting social ties, since it merely indicates whether an article is a self-citation. Thus, I did not include this variable in constructing the social ties scale but rather estimate its independent effect.
1 used the 1 0 0 articles dataset to compute the article size and the journal quality scales. For the social ties scale I used the 4950 paired articles dataset.
80 Scale Mean S.D. Min. Max. Alpha'
Article Size . 0 0 .95 -.77 7.72 .89
Recency of Knowledge . 0 0 .85 -1.47 2.58 .62
Theoretical Content . 0 0 .85 -.56 3.01 .62
Institutional Prestige . 0 0 . 8 6 -1.72 1.57 . 6 6
Author's Seniority . 0 0 .87 -1.38 2.50 .69
Author's Eminence . 0 0 .73 -.97 2.23 .13
Journal Quality . 0 0 .70 -1.26 3.73 .48
Social Ties . 0 0 .85 -.43 2.40 .60 'Reliability coefficients based on Cronbach's alpha.
Table 3.14. Descriptive Statistics and Reliability Coefficients for the Scales in Celestial Masers
Scale Mean S.D. Min. Max. Alpha'
Article Size . 0 0 .91 -1.44 4.16 .78
Recency of Knowledge . 0 0 .82 -.89 4.02 .50
Theoretical Content . 0 0 .82 -.54 3.13 .50
Institutional Prestige . 0 0 . 8 6 -1.92 1.29 . 6 6
Author's Seniority . 0 0 . 8 6 -1.38 3.28 . 6 6
Author's Eminence . 0 0 .90 -.58 3.76 .77
Journal Quality . 0 0 .78 -.91 2.76 . 6 8
Social Ties . 0 0 .79 - . 2 0 5.74 .40 'Reliability coefficients based on Cronbach's alpha.
Table 3.15. Descriptive Statistics and Reliability Coefficients for the Scales in Rational Expectations
81 with multicollinearity among the forty independent variables included in this exploratory model.
In a final step, I examined the zero-order correlations between the high, mean, and median scores of variables in articles with multiple authors. These correlations ranged fi"om .49 to .98. Furthermore, the correlations between the median and mean values were all between .94 and .98, suggesting that the results using either would be very similar to those using the other. Thus I decided to run the analyses in the rest of this dissertation using only high and median values because these two values tended to have the lowest correlations with each other and thus were most likely to provide different results.
However, actual results proved that using either high or median values produced similar overall conclusions. Consequently, and for the sake of parsimony, I decided to only present the results based on high values for cases with multiple authors.
The Regression Model
My network data violate two of the assumptions required for ordinary least squares estimation to produce best linear unbiased estimates. First, because the kth
potentially citing paper is implicated in- k 1 dyadic observations with the prior potentially
cited papers, it is likely that the disturbances among those k - 1 dyadic observations are correlated. Similarly, given N papers in a network, because every jth potentially cited paper is implicated in N-j dyadic observations with the subsequent potentially citing papers, it is also likely that the disturbances among those N-j dyadic observations are correlated. In order to correct for possible correlated disturbances for the multiple
82 observations from both the potentially citing papers and the potentially cited papers,1 use a two-way random effects generalized least squares (EGLS) regression model.^^ The two-way random effects model includes a random disturbance for the observations from each potentially citing article as well as a random disturbance for the observations from each potentially cited article. The random effects model for a two way design is:
yij = a + p'Xij + Ui + Wj + eÿ, where:
yjj is the dependent variable,
a is the intercept,
P ’xjj are the coefficients and values of the independent variables,
Uj is a random disturbance characterizing observations from the ith potentially
citing article,
Wj is a random disturbance characterizing observations from the j potentially
cited article,
E;j is a random disturbance for each combination of potentially citing and
potentially cited articles.
^^Using a random effect versus a fixed effect model is necessary since I have article characteristics of the potentially cited articles that remain constant within the group of dyadic observations that correspond to the same potentially cited article. The fixed effects estimator requires that there be within group variation in all variables for at least some groups (Greene 1993).
83 I used the program LIMDEP to estimate the random effects model. LIMDEP uses a two-step estimation procedure in which ordinary least squares (OLS) regression and/or group mean regression results are used to estimate the required error variances(Var (u,),
Var (Wj), and Far (eg)). In the second step, EGLS estimates are computed using the estimated variances (Greene 1993:484-485).
Another statistical problem is that because my dependent variable is dichotomous, the assumption of homoskedastic disturbances is not met (Hanushek and Jackson 1977).
There is currently no available statistical program that simultaneously (1) corrects for correlated disturbances via a two-way EGLS random effects model and (2) corrects for heteroskedasticity by specifying a non-linear functional form. This is explained by the fact that non-linear models do not explicitly include a disturbance term in the formal statistical model. As a consequence, there is no way to incorporate the non-independence of the observations into the model.
Goldberger (1964) developed an alternative approach to the joint problem of
correlated disturbances and heteroskedasticity by proposing a two-step, weighted
estimator to correct the problems of OLS regression associated with the linear probability
model (LPM). The LPM is a linear regression model with a dependent variable that is
either zero or one. One problem associated with performing OLS estimates with the
LPM is that the assumption of a constant variance for the disturbances cannot be
maintained since the variance of the disturbances vary systematically with the values of
the independent variables. This means that estimates of the sampling variances will not
be correct, and thus hypotheses testing based on these sampling variances will be
84 incorrect. Goldberger's correction is based on a two-step estimation procedure. The first step uses OLS regression results to estimate the required error variances. The second step uses these estimates to construct the set of weights, one for each observation, based on the reciprocals of the estimated standard errors of the estimated error variances. Thus,
Goldberger's formula for computing the weights is:
Wi = [l/(y)(l-y)]^
where "ÿ" are the predicted values of the dependent variable and should be between0 and
1. Applying those weights to all the variables in the equation, including the constant term, yields unbiased and efficient estimates (Aldrich and Nelson 1984). Thus, the standard errors of the second set of estimates are correct for performing hypothesis tests.
Goldberger's (1964) correction is problematic when estimates of the values of the dependent variable (y) are less than zero or greater than one, since predicted probabilities outside of the [0,1] interval have no meaning. When this problem is small, a practical solution is to set the out-of-range values to .001 or .999. However, when too many of the
estimated values fall outside the [0 , 1 ] range, there are strong reasons to believe that the model's fimctional form is non-linear (Aldrich and Nelson 1984; Hanushek and Jackson
1977).^ In this case, Goldberger's correction is of little help since it is based on the assumption of a linear model, with the implications it carries for the error distribution. In the celestial maser sample I found a fifth (19.03%) of predicted values outside of the [0,1]
^“’Aldrich and Nelson (1984:19) suggest that when more than 12 percent of the predicted values of the dependent variable are outside the [0,1] interval, Goldberger's (1964) correction will yield biased and unreliable estimates.
85 interval, and a third (32.24%) in the rational expectation sample, clearly high proportions.^® I performed preliminary random effects generalized least squares regression analyses applying Goldberger’s correction by setting the out of range values first at .001 and .999, then at .005 and .995, and finally at .01 and .99. The results (i.e., estimated regression coefficients and standard errors) obtained were vastly different from the ones obtained from the random-effects model without Goldberger’s correction.
Furthermore, while the results of the random effects model without the correction were consistent with those obtained using OLS, those obtained using Goldberger’s correction were not. In the Goldberger-corrected model, the estimated regression coefficients were sometimes three or four times the size of their OLS or uncorrected EGLS counterparts, leading to all the independent variables being significant at alpha=.05. Furthermore, the signs of these estimated coefficients were inconsistent with those obtained via OLS or uncorrected EGLS, and often making little theoretical sense. These results suggested that
Goldberger’s correction yielded biased and unreliable estimates and thus should not be used in my analyses.
As a result of the impossibility of simultaneously (1) correcting for correlated disturbances via a two-way random effects EGLS model, and (2) correcting for heteroskedasticity by specifying a non-linear functional form or by applying Goldberger's
(1964) correction, I was left with a difficult decision. Since there is no way to determine the extent to which correlated disturbances are a greater problem than heteroskedasticity.
®®These percentages are based on the predicted values obtained with the base models presented in chapters 4 and 5.
86 the best solution is to triangulate by comparing results from different statistical estimation procedures to assess the extent to which they lead to overall similar or different conclusions. In the rest of this dissertation I present the results from a random effects
EGLS model uncorrected for heteroskedasticity (this model corrects for correlated disturbances among the potentially citing and the potentially cited articles but not for heteroskedastic disturbances produced by a dichotomous dependent variable) and compare them to results obtained through logistic regression (this models corrects for heteroskedasticity by specifying a non-linear functional form, but not for correlated disturbances among the potentially citing and the potentially cited articles). If both methods give the same results, then one may feel more confident of the overall conclusions than if one used either of the two methods alone.
87 CHAPTER 4
ANALYSIS OF CITATION PATTERNS WITHIN
CELESTIAL MASERS
In this chapter I present results for the analyses I conducted on the network of celestial masers articles. I estimate several models of the determinants of citation links using both random effects generalized least squares and logistic estimation techniques and comparing their results. I conclude by reviewing my findings and discussing their implications for the role and functions of citations.
Regression Results
In order to examine the extent to which correlated disturbances among the citing- cited article dyads that correspond to the same citing article and among the citing-cited article dyads that correspond to the same cited article were a problem in my data, I computed the Lagrange Multiplier (LM). The Lagrange Multiplier tests the null hypothesis that the variances of the disturbances associated with a given group of dyadic observations (i.e., as either associated with the same citing or with the same cited article) do not significantly differ from zero. If the LM test is significant, we must reject the null hypothesis and conclude that correlated disturbances are a significant problem among that
88 specific group of observations and that the model needs to include a random error term for that group. On the other hand, if the LM test is not significant, we fail to reject the null hypothesis and conclude that the model does not need to include a random error term specific to that group of observations.
The value of the LM test for correlated disturbances among the citing-cited article dyads that correspond to the same citing article failed to be significant at a =.05
(LM=0.21, p=.65), indicating that I need not include a random error term to correct for the presence of correlated disturbances among this group of dyadic observations. In contrast, the LM test for correlated disturbances among the citing-cited article dyads that correspond to the same cited article was significant at a =.05 (LM=25.15, p<.000), indicating the need to include a random error term to correct for the presence of correlated errors among the dyads referring to the same cited article. This error structure suggests that features common to all of thecited articles (i.e., variables) may be missing firom the regression equation and thus need to be incorporated into the model by the inclusion of a random error term. In contrast, the lack of significance of the LM test for citing-cited article dyads corresponding to the same citing article suggests that I included all of the variables common to all of the citing articles.
Consequently, Table 4.1 presents a one-way random effects EGLS regression model correcting for the presence of correlated errors among the citing-cited dyads referring to the same cited article. This model regresses the likelihood of a citation link on characteristics of potentially citing and potentially cited articles and their corresponding authors and journals, as well as on relational variables indicating the
89 presence of various links between the citing and cited papers. This model includes all the
variables discussed previously as potential causes of citation links and will be used as a
base model to compare the determinants of citation links in celestial masers with those in
rational expectations.
[See Table 4.1]
Out of the forty independent variables, eleven are statistically significant at a=.05,
using one-tailed tests for variables for which I have specific directional hypotheses and two-tailed tests otherwise. They are, in order of magnitude as indicated by their
standardized regression coefficients, ( 1 ) whether the authors of the citing and the cited articles are the same, (2) the number of authors in the cited article, (3) the size o f the
citing article, (4) whether the citing and cited articles study the same subtopic, (5) the
number of authors in the citing article, (6 ) the quality of the cited article, (7) the time
elapsed between the citing and the cited articles, (8 ) whether the citing and the cited articles both claim to make a theoretical contribution, (9) the number of quantitative
tables and graphs per page in the citing article, 1( 0 ) the recency of the literature cited by
the citing article, and ( 1 1 ) the eminence of the citing author(s).'
'The pseudo in this and all subsequent EGLS models is computed by taking the squared correlation between the predicted values of the dependent variable and the observed values of the dependent variable. As such, it is a measure of the amount of variance in the observed values of the dependent variable that is explained by the predicted values of the dependent variable.
90 Variable b' s.e.‘ Beta
Citing Article Characteristics Number of Authors .551 * .146 .072 Book Chapter -.182 2.618 -.001 Article Size 1.846 + .305 .094 Recency of Knowledge 1.019 * .440 .036 Prop. Top Journal References -1.696 1.899 -.014 Equations per Page -.098 .201 -.010 Quantitative Tables per Page -1.683 * .815 -.037 Theoretical Content .035 .435 .002 Review -6.437 10.859 -.008 Critical -.262 1.729 -.002 Article Quality .046 .046 .016 Percent Female Authors .007 .021 .005 Percent Authors in University .005 .009 .010 Institutional Prestige -.468 .386 -.022 Author’s Seniority .390 .387 .017 Author's Eminence -.816 * .416 -.035 Journal Quality -.784 .611 -.023
Cited Article Characteristics Number of Authors .736 * .217 .099 Book Chapter .099 2.641 .001 Article Size .109 .511 .005 Recency of Knowledge .526 .557 .026 Prop. Top Journal References -3.546 2.474 -.037 Equations per Page -.050 .354 -.037 Quantitative Tables per Page -.831 1.328 -.018 Theoretical Content .273 .689 .012 Review -2.244 4.670 -.016 Critical -.698 2.417 -.008 Article Quality .160 + .078 .056 Percent Female Authors -.019 .037 -.011 Percent Authors in University .019 .014 .037 Institutional Prestige -.544 .628 -.024 Author’s Seniority .499 .574 .024 Author’s Eminence -.633 .849 -.020 Journal Quality .707 .657 .030
Relational Variables Same Journal 1.163 .764 .028 Both Theoretical 1.571 * .587 .041 Same Subtopic 3.306 + .678 .076 Same Authors 9.217 + 1.456 .108 Social Ties .403 .405 .018 Years Elapsed Between Articles -.267 + .106 -.046
Constant -1.724 2.681 Pseudo R^ .063 Lagrange Multiplier 25.15 * 'Unstandardized regression coefficients and their standard errors multiplied by 100. +Significant at p<.05 (one-tailed); ^Significant at p<.05 (two-tailed).
Table 4.1. One-Way Random Effects EGLS Regression Results for Celestial Masers ■ Base Model 91 The results obtained through logistic regression (presented in Table 4.2) including the same variables are similar to the EGLS results — with a couple of minor exceptions.
[See Table 4.2]
The logistic results identify two additional variables that are significant at a =.05 (one tailed): the quality of the cited article's journal and whether the citing and the cited article are both published in the same journal. The overall conclusions one would draw firom comparing both models are nevertheless similar.
Although this base model is an informative first step, it includes many insignificant variables for which I have no theoretical expectations, and thus is far firom parsimonious. Consequently, in Table 4.3 I present results firom a trimmed one-way random effects EGLS model correcting for correlated disturbances among the citing-cited article dyads that correspond to the same cited article in which I excluded variables which were both (1) insignificant in the earlier model, and (2) for which I had no theoretical expectations.^ I used results firom the base models in both celestial masers and rational expectations to inform the trimming because I wished to keep identical models in both areas in order to compare the effects of similar variables on the allocation of citations across disciplines.
[See Table 4.3]
^The non-significance of the Lagrange Multiplier test for the presence of correlated disturbances among the cited-citing article dyads that correspond to the same citing article (LM=1.06, p=.30) indicated that 1 need not include a random error term in the model for this group of observations.
92 Variable b s.e. Odds Ratio
Citing Article Characteristics Number of Authors .138 * .038 1.148 Book Chapter -.101 .805 .904 Article Size .223 + .057 1.249 Recency of Knowledge .273 ♦ .124 1.314 Prop. Top Journal References -.621 .590 .538 Equations per Page -.039 .068 .962 Quantitative Tables per Page -.559 * .273 .572 Theoretical Content .129 .151 1.138 Review -6.519 56.815 .002 Critical -.618 .793 .539 Article Quality .018 .013 1.018 Percent Female Authors -.001 .009 .999 Percent Authors in University -.001 .003 .999 Institutional Prestige -.079 .124 .924 Author’s Seniority .137 .128 1.147 Author's Eminence -.247 + .139 .781 Journal Quality -.220 .185 .803
Cited Article Characteristics Number of Authors .159 * .034 1.172 Book Chapter -4.735 6.135 .009 Article Size .041 .073 1.014 Recency of Knowledge .108 .108 1.114 Prop. Top Journal References -.800 .478 .450 Equations per Page -.043 .095 .958 Quantitative Tables per Page -.145 .257 .865 Theoretical Content .009 .136 1.009 Review 3.691 6.226 40.083 Critical -.105 .529 .901 Article Quality .033 * .012 1.033 Percent Female Authors -.082 .074 .922 Percent Authors in University .005 .003 1.005 Institutional Prestige -.169 .113 .845 Author's Seniority .158 .115 1.171 Author's Eminence -.085 .171 .919 Journal Quality .286 ♦ .108 1.331
Relational Variables Same Journal .345 + .201 1.412 Both Theoretical .526 * .204 1.692 Same Subtopic 1.378 + .272 3.966 Same Authors 1.054 + .309 2.870 Social Ties .062 .110 1.064 Years Elapsed Between Articles -.115 + .033 .891
Constant -4.939 * .676 Chi-Square 269.836 * -2 Log Likelihood 1302.77 +Significant at p<.05 (one-tailed); ^Significant at p<.05 (t\vo-tailed).
Table 4.2. Logistic Regression Results for Celestial Masers - Base Model
93 Variable b' s.e.‘ Beta
Citing Article Characteristics Number of Authors .509 ♦ .129 .067 Book Chapter -.003 2.504 .000 Article Size 1.915 + .284 .099 Recency of Knowledge 1.067 * .425 .038 Prop. Top Journal References -2.077 1.752 -.017 Quantitative Tables per Page -1.621 * .774 -.036 Theoretical Content .242 .378 .011 Review -7.748 .109 -.010 Author's Eminence -.854 * .377 -.037
Cited Article Characteristics Number of Authors .714 * .177 .097 Book Chapter .197 2.029 .003 Article Size .180 .424 .009 Recency of Knowledge .495 .442 .024 Prop. Top Journal References -3.274 1.983 -.034 Equations per Page -.044 .295 -.003 Quantitative Tables per Page -.758 1.078 -.017 Theoretical Content .117 .552 .005 Review -2.303 3.638 -.017 Article Quality .157 + .063 .055 Percent Female Authors -.020 .031 .012 Percent Authors in University .019 .011 .037 Institutional Prestige -.540 .514 -.024 Author’s Seniority .458 .458 .022 Author's Eminence -.624 .703 -.020 Journal Quality .839 + .521 .036
Relational Variables Same Journal .799 .713 .019 Both Theoretical 1.541 * .589 .040 Same Subtopic 3.110 + .657 .071 Same Authors 9.314 + 1.453 .109 Social Ties .319 .399 .014 Years Elapsed Between Articles -.297 + .098 -.051
Constant -.589 2.129 Pseudo R^ .063 Lagrange Multiplier 25.01 ♦ ' Unstandardized regression coefficients and their standard errors multiplied by 100. +Significant at p<.05 (one-tailed); *Significant at p<.05 (two-tailed).
Table 4.3. One-Way Random Effects EGLS Regression Results for Celestial Masers ■ Final Model
94 The results from Table 4.3 remain quite similar to those obtained in the base model. All the variables that were significant in the base model remain significant here.
In addition, the quality of the journal in which the cited article appeared becomes statistically significant at a=.05, using a one-tailed test.^
The relative importance of the variables, as indicated by their standardized regression coefficients, remains relatively unchanged, with the new statistically significant variable having only a weak relative explanatory power. Only seven independent variables have standardized regression coefficients above .05. They are, in
order of magnitude, (1 ) whether the authors of the citing and the cited articles are the same, (2) the size of the citing article, (3) the number of authors of the cited article, (4) whether citing and cited articles study the same subtopic, (5) the number of authors in the
citing article, (6 ) the quality of the cited article, and (7) the time elapsed between the citing and the cited articles. Finally, the results obtained via logistic regression are identical to those obtained with the trimmed one-way random effects EGLS regression and identify the same significant variables. For comparative purposes I present them in
Table 4.4.
[See Table 4.4]
^1 reestimated the model presented in Table 4.3 by including dummy variable indicators of mean substitution for variables that originally had missing data (i.e., the percent of female authors in the cited article and the percent of authors in university settings in the cited article). These variables were not statistically significant, indicating no differences between the mean-substituted cases and the rest of the cases. For parsimony's sake 1 did not present the model including these variables.
95 Variable b s.e. Odds Ratio
Citing Article Characteristics Number of Authors .146 • .033 1.157 Book Chapter .020 .776 1.020 Article Size .251 + .049 1.285 Recency of Knowledge .317 * .117 1.374 Prop. Top Journal References -.835 .553 .434 Quantitative Tables per Page -.476 + .261 .622 Theoretical Content .151 .127 1.163 Review -6.623 56.843 .001 Author's Eminence -.217 + .124 .805
Cited Article Characteristics Number of Authors .159 • .034 1.173 Book Chapter -4.752 6.133 .009 Article Size .036 .073 1.037 Recency of Knowledge .115 .105 1.122 Prop. Top Journal References -.816 .475 .442 Equations per Page -.050 .095 .951 Quantitative Tables per Page -.148 .252 .863 Theoretical Content .005 .134 1.005 Review 3.719 6.224 41.225 Article Quality .033 + .012 1.033 Percent Female Authors -.083 .075 .921 Percent Authors in University .005 .003 1.005 Institutional Prestige -.168 .113 .845 Author’s Seniority .148 .113 1.160 Author's Eminence -.094 .167 .910 Journal Quality .297 + .105 1.346
Relational Variables Same Journal .234 .188 1.263 Both Theoretical .535 * .202 1.708 Same Subtopic 1.332 + .268 3.789 Same Authors 1.084 + .306 2.957 Social Ties .058 .108 1.060 Years Elapsed Between Articles -.119 + .032 .888
Constant -4.846 ♦ .605 Chi-Square 264.208 ♦ -2 Log Likelihood 1308.398 +Significant at p<.05 (one-tailed); * Significant at p<.05 (two-tailed).
Table 4.4. Logistic Regression Results for Celestial Masers - Final Model
96 Discussion
First and foremost, the results presented above provide evidence that characteristics of the potentially citing articles and their related authors and journals do influence the citation process. Out of the twelve variables that significantly influenced the presence of a citation in celestial masers, five were characteristics associated with the potentially citing article. In addition, the benefits associated with using a network approach are exemplified by the number of relational variables that are significant. The importance of modeling the citation process as a relationship between a citing and a cited article is consistent with Small's (1978) argument that articles are given meanings by the individuals who cite them. In this sense, citers impart meaning to a given contribution by referring to it. The reference stands as a symbol for the idea the citer intends to convey to the reader. This argument suggests that the determinants of citations lie at least as much within the characteristics of the citing author and article as within the characteristics of the cited author and article. Thus, studies that leave out characteristics of the cited article and author are likely to miss a central aspect of the citation process.
Although I did not formulate specific hypotheses for every variable included in the final model presented in this chapter, in the following section I review the major
findings of my analysis of the celestial masers data in light of the hypotheses 1 had, and also offer some tentative explanations of some of the non-hypothesized findings that lend themselves to interpretation.
Among the characteristics of the citing article, the positive effect of article size
reflects the fact that longer articles tend to have more references, thus citing more. In
97 addition, the positive effect of number of authors might reflect the fact that each author has a specific repertoire of citations which may not necessarily overlap with that of the other contributing authors and which the author wants included in the paper. Thus articles with many authors may have a more varied list of references, indirectly increasing the likelihood of a citation link with another paper. Furthermore, the significant effects of the recency of the literature cited and the number of quantitative tables and graphs per page suggest that other indicators of the content of an article influence what it cites.
The negative effect of the eminence of the citing authors indicates that eminent astrophysicists tend to cite less than their less eminent colleagues. It could be that because of their central location in the information network of their field as a result of their eminence, they are more likely to know and cite only the most important work in their area and ignore more peripheral work (Breiger 1976). On the other hand, their citing less could be a manifestation of particularistic criteria. Since in celestial masers the article review process is single-blind (i.e., the reviewers know the name of the author), eminent scientists might believe that in virtue of their eminence more credit will be given to their argument and thus they do not need to cite as much as their less eminent counterparts in order to convince the reader of the validity of their claim. This latter
interpretation is consistent with a constructivist approach in which eminent authors can draw on their own eminence to persuade readers of the validity of their argument.
Turning to the characteristics of the cited article, the positive effect of the number of authors indicates that a paper with more than one author has a larger network of
98 potential readers who might know at least one of the authors, and this could increase the chance that a paper might be read and subsequently cited.
The positive and significant effect o f cited article quality net of author characteristics provides support for the normativists who argue that the quality of scientific contribution is a key determinant of whether it gets used or not (Hagstrom
1965; Kaplan 1965; Merton 1973). Although it does not have the strongest estimated effect, article quality plays an important role in accounting for the existence of a citation link between papers. Furthermore, the lack of a significant effect of any of the cited author characteristics (i.e., institutional prestige, eminence, percent female authors, seniority) further undermines the social constructivist argument that scientists try to cite the most influential authors in their field to boost their argument (Latour 1987).'*
The positive effect of journal quality probably results fi-om the fact that better journals are also the ones with the largest circulation. Thus, it makes sense that articles in journals that are widely distributed should tend to be cited more often since scientists are more likely to have their own copies of these journals. Furthermore, the fact that journal quality has an effect in an equation controlling for the quality of the cited article indicates that publication in a leading journal increases the chances of being cited regardless of the quality of one's contribution.
‘* 1 reestimated all the models in this chapter using the institutional prestige scale based on the 1993 5-point scale scores (from Goldberger et al. 1995) that would be similar to those used in the economics area (see my discussion in chapter 3). The results remained similar: institutional prestige of both citing and cited authors had no significant effect on the likelihood of a citation link.
99 Among the relational variables, the positive effects of both citing and cited articles claiming to make a theoretical contribution indicates that theoretical articles tend to overcite other theoretical articles. Furthermore, the positive effects of the subtopic variable indicates that, at least in celestial masers, articles are more likely to cite other articles directly relevant to their topic, net of their quality, than articles only loosely related to it.
The significant positive effect of articles written by the same authors reflects the fact that authors are very likely to use their own previous work. This is often because authors write more than one paper in an area and the subsequent papers tend to build upon the earlier work (see Baldi and Hargens 1995 for an example of how this was the case in N-rays research, a tum-of-the-century physics research area).* Furthermore, scholars also know their own papers, have copies of them at hand, and have a personal stake in citing them.
The lack of an effect of social ties indicates that scientists who know one another through graduate school or their workplace are not more likely to use each other's work than scientists without such ties. Hence, these results provide little evidence for the claim that social ties influence citation flows.
Finally, the negative effect of time elapsed between citing and cited articles is consistent with past research (Griffith and Small 1983; Price 1965, 1970). Price argued that research front work in the hard sciences is characterized by rapid incorporation of new knowledge manifested by an overcitation of recent work and rapid decay of
*My citing my study with Hargens provides an illustration of this argument.
100 references to old work. The negative effect in masers indicates that as contributions age they become less useful to scientists and thus become less likely to be cited.
Conclusion
In this chapter I presented regression results aimed at identifying the determinants of citations in the astrophysics research area. The findings indicated that, in addition to characteristics of cited articles, characteristics of potentially citing articles as well as relational variables indicating various ties between the citing and the cited paper significantly accounted for the existence of a citation between them. This suggests that my model is superior (i.e., better specified) to one attempting to explain citation counts only in terms of the characteristics of the cited articles.
Overall, the results indicated that article characteristics of both potentially citing and potentially cited articles tended to be more important in explaining the presence of a citation than author characteristics. The only finding consistent with a social constructivist argument is the significant negative effect of the citing author's eminence on the presence of a citation link, suggesting that eminent authors might draw on their own eminence to persuade readers of the validity of their claim. However, the lack of effect of characteristics of the cited author (e.g., eminence, institutional prestige, seniority, percent of female authors) suggests that the celestial masers area offers little evidence for the argument that people use citations as a tool of persuasion by allying themselves (i.e., citing) with the most eminent people in their field. Rather, the significant positive effects of cited article quality and content (e.g., whether citing and
101 cited articles both make a theoretical contribution or study the same subfields) are more in line with a normative account of the citation process.
102 CHAPTERS
ANALYSIS OF CITATION PATTERNS WITHIN
RATIONAL EXPECTATIONS
This chapter presents results of the analyses I conducted on the network of rational expectations articles. After estimating the regression models I discuss the findings and conclude by assessing their implications for arguments about the role and functions of citations in contemporary science.
Regression Results
As was the case for the astrophysics area, in a first step I estimated the Lagrange
Multiplier (LM) to test for correlated errors among the citing-cited article dyads that correspond to the same citing article and among the citing-cited article dyads that correspond to the same cited article. While the LM computed for correlated errors among the citing-cited article dyads that correspond to the same citing article failed to be significant (LM=0.00, p=.99), it was statistically significant for the citing-cited article dyads that correspond to the same cited article (LM=151.20, p<.000), suggesting that I needed to use a one-way random effects EGLS model that corrected for the presence of correlated errors among the dyads referring to the same cited article. As in the celestial
103 masers sample, this error structure suggests that features common to all of thecited articles may be missing from my model and need to be taken into account by the inclusion of a random error term.
1 present the results from this one-way random effects EGLS regression model in
Table 5.1. This model has the same variables as those included in the first celestial masers model (Table 4.1), and is presented as a basis for comparing the two areas.
[See Table 5.1]
Among the forty independent variables in Table 5.1, sixteen are significant, using one-tailed tests with a =.05 for variables for which I have directional hypotheses and two- tailed tests with a=.05 for variables for which I have no clear expectations. The effect of most of these variables is weak, with only half of them having a standardized regression coefficient of .05 or above. They are, in order of magnitude, (1) the quality of the cited article, (2) the size of the citing article, (3) whether the citing article is a book chapter, (4) whether both citing and cited articles are written by the same author, (5) whether the cited
article is a book chapter, ( 6 ) the institutional prestige of the cited author, (7) the eminence
of the citing author, and (8 ) the seniority of the cited author.
The results from the logistic regression including the same variables are presented in Table 5.2. With two exceptions they are consistent with the EGLS results. The
[See Table 5.2] size of the cited article is significant, and the time elapsed between citing and cited articles, while in the hypothesized direction, is insignificant. The results obtained by both estimation techniques lead to similar overall conclusions.
104 Variable b' s.e.‘ Beta
Citine Article Characteristics Number of Authors -.019 .399 -.001 Book Chapter 12.158 * 1.793 .102 Article Size 1.943 + .328 .104 Recency of Knowledge -.147 .331 -.007 Prop. Top Journal References 3.449 * 1.511 .037 Equations per Page -.031 .189 -.003 Quantitative Tables per Page -1.421 2.040 -.011 Theoretical Content -.649 ♦ .306 -.034 Review 2.202 2.382 .015 Critical 1.257 .743 .029 Article Quality .037 .050 .015 Percent Female Authors -.011 .012 -.014 Percent Authors in University .007 .007 .016 Institutional Prestige .377 .361 -.018 Author's Seniority .151 .318 .007 Author's Eminence -1.206 ♦ .431 -.053 Journal Quality -.341 .362 -.016
Cited Article Characteristics Number of Authors -.496 .610 -.015 Book Chapter -5.302 * 1.537 -.074 Article Size -.028 .412 -.002 Recency of Knowledge .823 + .400 .041 Prop. Top Journal References 3.695 + 1.841 .040 Equations per Page .414 .247 .036 Quantitative Tables per Page -4.600 2.504 -.039 Theoretical Content 1.305 ♦ .448 .057 Review -4.023 + 2.260 -.038 Critical 1.297 .892 .030 Article Quality .447 + .033 .343 Percent Female Authors -.012 .219 -.009 Percent Authors in University -.002 .010 -.003 Institutional Prestige 1.215 + .426 .065 Author's Seniority .986 + .419 .050 Author's Eminence -2.023 .442 -.119 Journal Quality -1.398 .130 -.064
Relational Variables Same Journal 4.000 * 1.339 .040 Both Theoretical .611 .484 .018 Same Subtopic -.176 .828 -.003 Same Authors 18.329 + 3.426 .087 Social Ties -.331 .357 -.016 Years Elapsed Between Articles -.295 ♦ .123 -.049
Constant -3.781 * 1.876 Pseudo R^ .145 Lagrange Multiplier 151.20 * 'l/nstandardized regression coefficients and their standard errors multiplied by 100. +Significant at p<.05 (one-tailed); ‘ Significant at p<.05 (two-tailed).
Table 5.1. One-Way Random Effects EGLS Regression Results for Rational Expectations • Base Model 105 Variable b s.e. Odds Ratio
Citine Article Characteristics Number of Authors .016 .223 1.016 Book Chapter 1.516 * .501 4.554 Article Size .855 + .139 2.352 Recency of Knowledge -.198 .161 .820 Prop. Top Journal References 1.747 * .689 5.737 Equations per Page -.180 .112 .835 Quantitative Tables per Page -.695 .968 .499 Theoretical Content -.389 * .161 .677 Review -.945 .722 .389 Critical .230 .344 1.258 Article Quality .023 .017 1.023 Percent Female Authors -.003 .007 .997 Percent Authors in University .005 .004 1.005 Institutional Prestige -.241 .159 .786 Author's Seniority .086 .149 1.090 Author’s Eminence -.338 * .179 .714 Journal Quality -.242 .157 .755
Cited Article Characteristics Number of Authors -.296 .271 .744 Book Chapter -2.276 * .704 .103 Article Size .745 + .173 2.106 Recency of Knowledge .289 + .145 1.335 Prop. Top Journal References 2.519 + .905 12.420 Equations per Page -.004 .125 .996 Quantitative Tables per Page -2.339 1.416 .091 Theoretical Content .312 + .193 1.366 Review -3.422 • 1.311 .033 Critical .211 .459 1.235 Article Quality .073 + .008 1.076 Percent Female Authors .013 .010 1.013 Percent Authors in University .010 .008 1.010 Institutional Prestige .603 + .194 1.383 Author's Seniority .357 + .221 1.430 Author's Eminence -.480 .185 .619 Journal Quality -.392 .216 .676
Relational Variables Same Journal 1.205 * .425 3.338 Both Theoretical .280 .203 1.324 Same Subtopic -.176 .355 .839 Same Authors 2.805 + 1.029 16.521 Social Ties -.175 .166 .840 Years Elapsed Between Articles -.054 .052 .947
Constant -7.614 ♦ 1.165 Chi-Square 462.799 * -2 Log Likelihood 846.73 +Significant at p<.05 (one-tailed); ^Significant at p<.05 (two-tailed).
Table 5.2. Logistic Regression Results for Rational Expectations - Base Model
106 Because of the large number of insignificant variables for which I have no clear theoretical justification in the base model, in Table 5.3 I present results from a trimmed model that excludes such variables. To be consistent, I only excluded variables which were both insignificant and for which I had no theoretical expectations in both rational expectations and celestial masers. Again, the results of the Lagrange Multiplier test indicated no problem with correlated disturbances among the among the citing-cited article dyads that correspond to the same citing article (LM=0.10, p=.75) but that a random disturbance term should be included to correct for the presence of correlated errors among the dyads referring to the same cited article (LM=155.69, p<.000). Thus
Table 5.3 presents results from a one-way random effects EGLS regression model that corrects for correlated disturbances among the citing-cited dyads that correspond to the same cited article.
[See Table 5.3]
These results remain quite similar to those obtained with the base model. Two new variables that were insignificant using two-tailed tests with a =.05, the number of equations per page in the cited article (positive effect) and the number of quantitative tables and graphs per page in the cited article (negative effect), now become significant.
In addition, one variable that was significant in the previous model, the proportion of top journal references in the citing article, becomes insignificant in this model, using a two- tailed test with a=.05. Furthermore, the relative contribution of each variable to the model, as indicated by its standardized regression coefficient, remains unchanged for the previously significant variables. Finally, results from a logistic regression using the same
107 Variable b' s.e.' Beta
Citine Article Characteristics Number of Authors -.163 .364 -.006 Book Chapter 12.557 * 1.744 .106 Article Size 1.913 + .304 .102 Recency of Knowledge -.259 .312 -.012 Prop. Top Journal References 2.469 1.317 .026 Quantitative Tables per Page -1.830 1.835 -.014 Theoretical Content -.676 * .298 -.035 Review 2.077 2.226 .014 Author’s Eminence -1.042 * .353 -.046
Cited Article Characteristics Number of Authors -.453 .498 -.014 Book Chapter -5.616 * 1.180 -.079 Article Size -.138 .322 -.007 Recency of Knowledge .803 + .319 .040 Prop. Top Journal References 3.224 + 1.412 .035 Equations per Page .381 ♦ .187 .033 Quantitative Tables per Page -6.007 ♦ 1.929 -.051 Theoretical Content 1.428 * .364 .063 Review -4.133 * 1.748 -.039 Article Quality .454 + .024 .349 Percent Female Authors -.015 .019 -.011 Percent Authors in University -.001 .008 -.003 Institutional Prestige 1.304 + .337 .070 Author's Seniority .925 + .326 .047 Author's Eminence -2.076 .346 -.122 Journal Quality -1.412 .340 -.065
Relational Variables Same Journal 3.665 * 1.338 .037 Both Theoretical .418 .484 .012 Same Subtopic -.201 .775 -.004 Same Authors 18.072 + 3.410 .086 Social Ties -.363 .351 -.017 Years Elapsed Between Articles -.306 * .103 -.051
Constant -1.617 1.472 Pseudo R^ .143 Lagrange Multiplier 155.69 * 'Unstandardized regression coefficients and their standard errors multiplied by 100. +Significant at p<.05 (one-tailed); ^Significant at p<.05 (two-tailed).
Table 5.3. One-Way Random Effects EGLS Regression Results for Rational Expectations ■ Final Model
108 variables are quite similar to those obtained via EGLS, as indicated by Table 5.4. The differences are that in the logistic model — as compared to the EGLS model — two additional variables are significant, the proportion of top journal references in the citing article and the cited article's length, and one variable that was significant in the EGLS model, the number of equations per page, is insignificant here.
[See Table 5.4]
Discussion
As was the case with the celestial masers data, characteristics of both the potentially citing and potentially cited articles helped explain the presence of a citation in the rational expectations area. In addition, adopting a social network approach by including relational variables also improved our ability to predict citation links. These findings further confirm the importance of treating the citation process as a network of citation flows in which both the citing and cited articles and authors play a role in
determining what does or does not get cited.
Among the characteristics of the citing article, the positive effect of article size
indicates that longer articles tend to cite more, largely as a result of having more
opportunities to cite since they are longer. Furthermore, in the rational expectations
109 Variable b s.e. Odds Ratio
Citine Article Characteristics Number of Authors -.095 .201 .910 Book Chapter 1.980 * .442 7.245 Article Size .838 + .130 2.312 Recency of Knowledge -.273 .160 .761 Prop. Top Journal References 1.695 * .635 5.446 Quantitative Tables per Page -.260 .836 .771 Theoretical Content -.343 ♦ .143 .710 Review -.859 .628 .424 Author’s Eminence -.253 + .143 .776
Cited Article Characteristics Number of Authors -.320 .267 .726 Book Chapter -2.371 * .690 .093 Article Size .743 + .174 2.103 Recency of Knowledge .276 + .143 1.318 Prop. Top Journal References 2.470 + .899 11.817 Equations per Page -.020 .110 .980 Quantitative Tables per Page -2.369 + 1.268 .094 Theoretical Content .324 + .189 1.382 Review -3.474 * 1.292 .031 Article Quality .074 + .008 1.077 Percent Female Authors .013 .010 1.013 Percent Authors in University .010 .008 I.OlO Institutional Prestige .663 + .194 1.940 Author's Seniority .370 + .202 1.448 Author's Eminence -.520 .181 .595 Journal Quality -.438 .213 .646
Relational Variables Same Journal 1.112 * .411 3.039 Both Theoretical .263 .203 1.300 Same Subtopic -.243 .341 .785 Same Authors 3.186 + 1.016 24.194 Social Ties -.236 .161 .790 Years Elapsed Between Articles -.075 + .046 .928
Constant -6.947 * 1.093 Chi-Square 449385 ♦ -2 Log Likelihood 860.144 +Significant at p<.05 (one-tailed); ^Significant at p<.05 (two-tailed).
Table 5.4. Logistic Regression Results for Rational Expectations - Final Model
1 1 0 literature, book chapters tend to be overviews or summaries of literature in a specific area and this explains their citing more.'
The negative effect of articles claiming to make a theoretical contribution
indicates that theoretical articles are less likely to cite than articles that mostly report empirical findings, regardless of their length. Finally, the significant negative effect of the citing author's eminence indicates that, like in celestial masers, highly cited scientists
tend to cite fewer papers in the previously published literature than their lesser colleagues.
Turning to the characteristics of the cited article, the negative effect of publication
in an edited volume and of the article being a review suggests that this type of article is
less likely to be cited in economics. This appears to be contradictory to Price's (1965)
argument that review papers are more likely to be cited because they summarize past
literature and thus discharge the author fi-om having to cite all the literature that has come
before the review. However, Price's evidence was based on a single sample of a tum-of-
the-century physics area, and it is possible that in economics reviews and book chapters
serve a different function than in the hard sciences. For example, Bazerman (1988)
showed that authors in the social sciences spend much of the article situating their
argument in light of previous literature, often by criticizing it. This pattern suggests that
social scientists might not find reviews useful in building their arguments because, by
summarizing earlier work, reviews do not help them to properly "situate" their
contribution and critique early ideas.
'The four rational expectations papers in my sample that were published in an edited volume have a mean number of pages of 29.5 and a mean number of references of 29. These numbers are much higher than the mean for the entire sample.
Ill The significant estimated effects of the recency of knowledge, the proportion of references from top journals, the number of equations per page, the number of quantitative tables and graphs per page, and whether the article claims to make a theoretical contribution provide evidence that the cognitive content of the cited article is an important determinant of whether it gets cited. Clearly, at least in economics, there seems to be a premium on articles using recent contributions as well as knowledge building upon contributions published in the best journals, possibly because authors use journal quality as a proxy for article quality or because they are more familiar with the best journals (Glenn 1971).
Furthermore, the positive effects of the number of equations per page^ and theoretical articles, and the negative effect of quantitative tables and graphs per page, seem to support the view that economics is a field where empirical articles are given low priority and little prestige compared to those filled with sophisticated mathematical models. Leontief (1983:x) showed that the majority of articles published in one of the most respected and prestigious of economics journals, theAmerican Economic Review, tended to contain mathematical models without any data. He further argued that empirical, policy-oriented, or problem-solving articles tended to be given low prestige in economics. The significant positive effects of equations and theoretical articles, and the significant negative effect of quantitative tables and graphs per page reflect this tendency.
That the significant positive estimated effect of cited article quality is the strongest determinant of the presence of a citation provides support for the normativist
^This variable is only significant in the EGLS model.
112 argument that article quality is one of the crucial — if not the crucial — elements of whether a scientific contribution is used by working scientists. However, the significant positive effects of institutional prestige and seniority — net of article quality — suggest that particularistic criteria play at least some part in the allocation of credit in the social sciences. Specifically, the fact that institutional prestige is significant while author eminence is not indicates that being affiliated with a prestigious institution is more important than being a highly cited scientist for having one's contribution cited. Thus, maybe the two systems of allocation of rewards in science should not be "what one says" versus "who one is" but rather "what one says" versus "where one is" as Reskin (1977) and Long (1978) argued.
Finally, turning to the relational variables, the positive effect of both citing and cited articles being published in the same journal indicates that articles overcite other articles published in the same journal. This finding might reflect the fact that scientists often try to include references to articles published in the same journal as the one they intend to send their contribution to in order to "signal" the editor or reviewers that their article is appropriate for that journal. Alternatively, it is also possible that journals focus on specific topics at the exclusion of others so that, for example, theJournal o f Finance publishes more papers on finance that cite other finance papers.
The significant positive effect of whether the citing and cited articles are written by the same authors indicates that authors are likely to overcite their own work, possibly because they are building upon their own earlier contributions. On the other hand, the lack of effect of the existence of social ties among citing and cited authors undermines the
113 argument that people who have institutional ties are more likely to use one another's work.
Finally, the significant negative effect of time elapsed between citing and cited articles indicates that as a contribution ages, it is less likely to be cited by subsequent articles. This finding contradicts Price's (1965,1970) argument that social science literatures are equally likely to build upon classic works than upon recent knowledge and suggests that knowledge may be equally cumulative in progressive research areas, regardless of their position on a hard/soft dimension.^
However, it is possible that the effect of time elapsed between citing and cited articles is U-shaped in economics. Accounts of literature usage in the social sciences
(Bazerman 1988; Cozzens 1985) suggest that scholars in those fields cite "classic" and recent contributions more than "middle-aged" papers. I tested this argument by adding a squared term for time elapsed between citing and cited articles to the model presented in
Table 5.4. The effect of time elapsed between citing and cited articles became insignificant and the squared term was statistically significant but negative. While this finding does not support the argument for the existence of a U-shaped relationship, it
^In fairness to Price, I must note that his argument about literature aging did not take into consideration the quality of the contributions in an area. When I excluded the cited article quality variable from the model presented in Table 6.3, time elapsed between citing and cited articles failed to reach statistical significance.
114 indicates that the effect of literature aging on the likelihood of a citation is non-linear in rational expectations and follows an inverted U-shaped pattern/
Conclusion
In this chapter I presented results for the analysis of the field of rational expectations. Using results from both EGLS and logistic estimation techniques I showed that conceptualizing the citation process as a dyadic relationship between a potentially citing and a potentially cited articles is just as important for understanding the determinants of citations in economics as it was for astrophysics.
Results from the regression analyses indicated that the cognitive content of both the citing and the cited articles are important in accounting for which articles get cited. In particular, cited article quality is the single best predictor of the existence of a citation link between two articles. These findings support the normativists who claim that content and perceived quality are the central determinants of literature use. However, I also documented positive and significant effects of functionally irrelevant cited author characteristics like institutional prestige and seniority, suggesting that "who one is" partly helps account for the allocation of recognition — at least in economics. Thus results from this area indicate that social constructivists might be partly correct in suggesting that when confronted with several choices, authors will tend to ally themselves with the most
"prestigious" authors in their discipline. However, it is likely that this finding reflects
‘‘I tested this argument for celestial masers as well. The squared term failed to reach significance, suggesting that the effect of literature aging on citation likelihood is linear in the astrophysics area.
115 differences in the level of codification and agreement over what constitutes quality work
across the hard and soft sciences, rather than a process of prestige enhancement on the
part of the citing authors. If the latter were correct, then there would be no reason why
the effect of institutional prestige and seniority should be different across scientific areas.
Overall, the results of the rational expectations analyses provide evidence for the joint existence of an achievement and an ascription process in economics in which
citations are distributed mostly on the basis of what one says, as indicated by the strong
effect of cited article quality and other indicators of cognitive content, but also partly on
the basis of who one is, as indicated by the small but significant positive effects of
institutional prestige and seniority.
116 CHAPTER 6
COMPARING THE DETERMINANTS OF CITATIONS IN THE
NATURAL AND THE SOCIAL SCIENCES
The previous two chapters reported separate results for celestial masers and rational expectations. In this chapter I use these results to answer the question of the extent to which the determinants of citations are the same or different across these two research areas. I first examine the overall structure of each area as indicated by their descriptive characteristics, and then move on to a comparison of the regression results. I conclude by discussing both sets of results in light of theories of differences across scientific disciplines.
Comparing the Overall Structure of the Areas
We can gauge differences in the basic organization and conduct of science in each of the two areas by an initial comparison of their descriptive statistics. Sociologists of science have argued for quite some time that as a result of the different nature of the topics studied (Price 1965, 1970), the amount of funding received (Liebert 1976), the differing importance of technological tools (Fuchs 1992), and the differing amount of consensus over basic issues (Hargens 1975) and codification of knowledge (Zuckerman
117 and Merton 1971,1972), science is organized and conducted differently in the hard and the soft sciences.
Derek Price was among the first to hypothesize as well as empirically study specific differences in the overall structure of scientific disciplines. In his landmark book
Little Science, Big Science (1963:87-91), he argued that the massive increase in multiple- authored papers evolved as a way to cope with the exponential growth of the number of scientists and scientific contributions. In fields that have a high level of codification and where the work of scientists is highly dependent upon earlier technical discoveries, multiple authorship is a way to increase efficiency in research by partitioning the various task among several authors. This has the result of limiting the amount of trivial research being done by allowing lower-level scientists to work under the leadership of higher-level ones. This scenario, however, is more likely to be applied in fields where the problems to be solved are well defined and there is consensus over how to solve them.
Price (1963,1965) also documented differences across disciplines in the extent to which they build upon recent knowledge. Because the natural sciences are more cumulative than the social sciences — that is they are more dependent upon earlier discoveries to produce new knowledge — scholars working in those fields tend to pay greater attention to recent contributions. Thus, papers in the natural sciences are more likely to cite recent, or even forthcoming references — as a result of the widespread diffusion of preprints — than articles in the social sciences.
Merton's (1973) work concerned itself with how the practices of scientists vary across hard and soft disciplines. Specifically, Merton showed how differences in the
118 level of codification across fields lead to greater competition for priority in discoveries in the hard sciences than the social sciences. His argument suggests that as a result of a greater premium on novelty, the publication system in the natural sciences should be more efficient — that is, quicker — in disseminating new contributions than in the social sciences, where the concern over priority is low.
Although more concerned with the rhetorical differences in article writing across disciplines than their actual structural differences, Bazerman (1988) nevertheless demonstrated real differences in the shape and content of articles in the natural and the social sciences. Bazerman's comparison of physics and political science showed that articles in political science tended to be much longer, have more references, and spend much more time in criticizing the earlier literature than articles in physics. He argued that this is the result of differences in codification in the two fields. Because in political science there is no clear consensus over the meaning of the earlier literature, much time is
spent in reinterpreting the past literature. This leads to lengthy introductions with many references to earlier work in order to situate the present contribution. In contrast, in physics there is little disagreement over the interpretation of earlier contributions. Thus,
physics authors spend little time reviewing the earlier literature, let alone critiquing or
reinterpreting it.
To the extent that the documented or hypothesized differences across scientific
fields just reviewed are correct, such differences should be reflected, at least in part, by
differences in basic descriptive characteristics of articles and authors in each of the two
areas studied in this dissertation. Tables 6.1 and 6.2 report the means of variables
119 included in the analyses conducted in chapters 4 and 5 for each of the two areas and tests
the null hypothesis that they are not significantly different across areas. Table 6 .1
[See Tables 6.1 and 6.2] presents means based on the 100 articles for each area and Table 6.2 reports means for the dependent and relational variables based on the 4950 pairs of potentially citing and potentially cited articles.
Most of the differences documented in Tables 6.1 and 6.2 are consistent with past arguments and suggest the existence of real differences in the production of articles in the natural and the social sciences. Starting with the characteristics associated with the 100 articles, we can see that in the celestial masers literature the mean number of authors contributing to an article is 3.20 while in rational expectations it is only 1.31. This difference is statistically significant at alpha=.05, using a two-tailed test. Furthermore, in the astrophysics area articles are significantly less likely than in the economics area to be written by full professors or their equivalent.
These differences are consistent with those documented by Price (1963) and can be understood in terms of the organization of work in each of the two areas. Because the tasks to be done are more clearly defined in the natural sciences (Hargens 1975), multiple authorship can be seen as mechanism to increase the efficiency of scientific research by partitioning the work to be done among several authors. A comparable division of labor in the social sciences would be harder in the absence of clearly defined problems and clear consensus over how to solve them (Storer 1967; Zuckerman and Merton 1972). In addition, as a result of the greater technical nature of natural science research, much work
120 Celestial Masers Rational Expectations Variable Mean S.D. Mean S.D. T Ratio
Number of Authors 3.20 2.54 1.31 .58 7.25 * Book Chapter .05 .26 .04 .20 .30 Number of References 25.08 33.25 19.33 14.22 1.59 Prop. Recent References .51 .24 .29 .22 6.76 * Prop. Top Journal References .54 .18 .33 .18 8.25 * Prop. Forthcoming References .05 .06 .02 .05 3.84 * Number of Pages 9.60 10.92 14.51 8.21 -3.59 * Equations per Page .93 1.67 1.72 1.44 -3.58 * Quantitative Tables per Page .59 .42 .08 .14 11.52 * Theoretical Figures per Page .06 .14 .03 .08 1.86 Theoretical Purpose .32 .47 .34 .48 -.30 Review Article .01 .10 .02 .14 -.58 Critical Article .04 .20 .19 .39 -3.42 * Article Quality 6.34 6.67 6.75 10.88 -.32 % Female Authors 2.30 12.65 3.67 17.66 -.63 % Authors in Universities 76.33 36.22 84.92 35.03 -1.70 Author's Ph.D. Prestige' 3.94 .92 4.10 1.07 -1.13 1996 - Author’s Year of Ph.D. 34.00 8.50 24.65 8.20 7.92 * % Full Professors 36.26 34.35 51.11 47.98 -2.52 * Employment Prestige' 3.66 .93 3.29 1.01 2.69 * Author’s Eminence 39.72 35.42 25.72 46.17 2.41 * Author’s Previous Area Articles 5.93 7.25 1.14 1.87 6.40 * Publication Ownership .73 .45 .36 .48 5.62 * Journal Circulation 5099.90 17106.63 6325.59 6829.34 -.67 Joiunal Quality 3.23 1.62 .86 .71 13.40 * ♦Significant at alpha=.05, two-tailed. 'Means and standard deviations based on Goldberger et al. (1995) prestige scores using same metric as Economics.
Table 6.1. Means Comparisons for Variables in 100 Celestial Masers and 100 Rational Expectations Articles
121 Celestial Masers Rational Expectations Variable Mean S.D. Mean S.D. T Ratio
Deoendent Variable Citation Occurrence .04 .19 .03 .17 2.76 *
Relational Variables Same Journal Publication .29 .45 .03 .17 38.03 * Both Theoretical .56 .50 .55 .50 .99 Same Subtopic .75 .43 .90 .30 -20.13 * Same Authors .05 .22 .01 .08 12.02 * Work Colleagues .12 .32 .02 .13 20.37 * Same Ph.D. Institution 20 .40 .07 .25 19.39 ♦ Years Elapsed Between Articles 4.54 3.27 3.13 2.82 22.97 * * Significant at alpha=.05, two-tailed.
Table 6.2. Means Comparisons of Dependent and Relational Variables for 4950 Pairs of Potentially Citing/Potentially Cited Articles in Celestial Masers and Rational Expectations
122 in astrophysics is done with the help of technicians who tend not to be full professors, or by individuals with non-academic appointments.
Table 6.1 also indicates significant differences in the shape and content of articles in the natural and social sciences. Articles in astrophysics are shorter by about five pages,' and contain significantly greater proportions of recent references, references to articles published in top journals, and forthcoming references. Astrophysics articles are also significantly less critical of the literature in their area than those in economics. Most of these differences are consistent with the arguments reviewed above and can be explained by differences in levels of codification, dependence upon new knowledge, and consensus over the interpretation of earlier work.
That economics articles do not contain significantly more references than astrophysics articles is contrary to Bazerman's (1988) findings. It is possible that rational expectations is atypical of economics research areas in general. After all, rational expectations has been characterized as a progressive and successftd area (Attfield et al.
1985; Miller 1994; Shaw 1984). This suggests that rational expectations may exhibit
'To test the possibility that the page size was significantly different across the two areas I took a random sample of 10 articles in each area and, for each article, counted the number of words contained in its first full page of text (i.e., a page that did not have
figures or tables). 1 then tested whether the mean number of words per page was significantly different across areas by computing a t-test. The mean number of words per page was 866.60 with a standard deviation of 156.98 in astrophysics as compared to 544.20 with a standard deviation of 99.37 in rational expectations. The t value was 5.49, indicating that pages in astrophysics journals contained significantly more words than pages in economics journals. Thus, we should exercise caution when interpreting the page length difference across areas reported above. Articles in economics may be longer as measured by page number, but not necessarily longer as measured by word counts.
123 greater consensus than other social science research areas. For example, in his analysis of articles published in the American Political Science Review, the flagship journal of the political science discipline, Bazerman (1988:282) found that the articles had, on average, thirty references, a much higher number than is the case for rational expectations. Thus, possibly as a result of greater consensus over important issues in the area, authors working in rational expectations might not need to spend as much time reinterpreting the literature, a practice that generates many references.
Equations are more prevalent in economics but quantitative tables and graphs are more prominent in astrophysics. This makes sense in light of the argument that economics is a discipline keen on sophisticated mathematical models but much less interested in empirical evidence (Leontief 1983; Morgan 1988).
While little past research would help us anticipate those findings, the results in
Table 6.1 also indicate that authors in celestial masers tend to receive more citations, tend to write more articles in the area, tend to work at more prestigious institutions, and received their Ph.D.s at an earlier time than authors in rational expectations.
Finally, a look at the means for journal characteristics indicate that in astrophysics journals are more likely to be owned by a professional association, and overall tend to be of higher quality (as measured by the JCR impact factor) than in economics.
Turning to the results presented by Table 6.2 we can also see significant differences among the dependent and relational variables for the two areas. Articles in celestial masers are significantly more likely to be cited by other articles in the sample than articles in rational expectations. There are also significantly more pairs of
124 potentially citing/cited articles written by the same authors in masers than in rational expectations. This is most likely the result of the greater number of authors per paper in astrophysics as compared to economics, thus increasing the chance that an author will be the same. In addition, many more pairs of papers in astrophysics are written by authors having either worked at the same place or received their Ph.D.s from the same institution than is true in economics. This reflects the smaller number of institutions granting doctorates in astrophysics as well as the smaller number of employing institutions for astronomers as compared to economics.^
Significantly more pairs of articles are from the same journal in astrophysics than in economics. This reflects the fact that fewer journals published masers articles than rational expectations ones. The 100 masers articles were published in twenty journals and three books. In comparison, the 100 rational expectations articles were published in forty journals and four books.
Significantly more pairs of papers are on the same subtopic in rational expectations than in celestial masers. Finally, the average time gap between the potentially citing and the potentially cited articles is significantly higher in astrophysics than in economics, indicating a different distribution of the number of articles published in a given year by field.^
^There were 37 Ph.D.-granting institutions and 46 employing institutions in celestial masers for a total of 320 authors. In contrast, there were 40 Ph.D.-granting institutions and 73 employing institutions in rational expectations for 131 authors.
^This finding reflects the fact that more papers were published at the beginning (continued...)
125 Overall, the comparison of descriptive statistics for both areas suggests differences in the organization and conduct of science in the natural and social sciences that are not dissimilar to those hypothesized by sociologists and historians of science.
Specifically, the patterns exhibited by both areas are consistent with accounts of the natural sciences as more codified and sharing greater consensus over basic issues and problems than the social sciences. The only deviation fi-om the hypothesized differences has to do with the lack of a significant difference in the number of references per paper in the two areas, suggesting that rational expectations may be more codified than other social science areas.
Do the Determinants of Citations Differ among Two Research Areas in the Natural and the Social Sciences?
Although the way science is organized and conducted may differ across disciplines, this in itself is no evidence that the determinants of citations also differ by area. This is the issue I address in the following section by comparing results of regression analyses conducted on celestial masers and rational expectations. Table 6.3 compares the results of the final regression models for both areas as they were presented
in chapters 4 and 5 (Tables 4.3 and 5.3).
[See Table 6.3]
^(...continued) and at the end of the time period studied in celestial masers than in rational expectations, thus creating a larger time gap, on average, between each pair of potentially citing-cited papers.
126 Celestial Masers Rational Expectations T Ratio Variable b' s.e.‘ b ' s.e.' b^-bi^O
Citine Article Characteristics Number of Authors .509 • .129 -.163 .364 1.74 + Book Chapter -.003 2.504 12.557 * 1.744 -4.12 * Article Size 1.915 + .284 1.913 + .304 .00 Recency of Knowledge 1.067 • .425 -.259 .312 2.51 * Prop. Top Journal References -2.077 1.752 2.469 1.317 -2.07 * Quantitative Tables per Page -1.621 * .774 -1.830 1.835 .10 Theoretical Content .242 378 -.676 ♦ .298 1.91 + Review -7.748 .109 2.077 2.226 -4.41 • Author’s Eminence -.854 * .377 -1.042 * .353 .36
Cited Article Characteristics Number of Authors .714 * .177 -.453 .498 2.21 ♦ Book Chapter .197 2.029 -5.616 • 1.180 2.48 * Article Size .180 .424 -.138 .322 .60 Recency of Knowledge .495 .442 .803 + .319 -.56 Prop. Top Journal References -3.274 1.983 3.224 + 1.412 -2.67 * Equations per Page -.044 .295 .381 ♦ .187 -1.22 Quantitative Tables per Page -.758 1.078 -6.007 • 1.929 2.38 * Theoretical Content .117 .552 1.428 * .364 -1.98 * Review -2.303 3.638 -4.133 * 1.748 .45 Article Quality .157 + .063 .454 + .024 -4.39 * Percent Female Authors -.020 .031 -.015 .019 -.14 Percent Authors in University .019 .011 -.001 .008 1.46 Institutional Prestige -.540 .514 1.304 + .337 -3.00 * Author’s Seniority .458 .458 .925 + .326 -.83 Author's Eminence -.624 .703 -2.076 .346 1.85 + Journal Quality .839 + .521 -1.412 .340 3.62 *
Relational Variables Same Journal .799 .713 3.665 ♦ 1.338 -1.89 + Both Theoretical 1.541 * .589 .418 .484 1.47 Same Subtopic 3.110 + .657 -.201 .775 3.26 * Same Authors 9.314 + 1.453 18.072 + 3.410 -2.36 * Social Ties .319 .399 -.363 .351 1.28 Years Elapsed Between Articles -.297 + .098 -.306 * .103 .06
Constant -.589 2.129 -1.617 1.472 .40 Pseudo .063 .143 Lagrange Multiplier 25.01 * 155.69 * 'Unstandardized regression coefficients and their standard errors multiplied by 100. +Significant at p<.05 (one-tailed); * Significant at p<.05 (two-tailed).
Table 6.3. Comparison of One-Way Random Effects EGLS Regression Results for Celestial Masers and Rational Expectations
127 Five independent variables are significant in both celestial masers and rational expectations. They are the citing article's size, the eminence of the citing author, the quality of the cited article, whether the citing and cited authors are the same people, and the time elapsed between the citing and the cited article. A look at their regression coefficients indicates that the effects are in the same direction, suggesting that some common patterns of citation determinants exist across disciplines.
First, longer articles tend to cite more, presumably because they have more opportunity to cite given their length. Second, eminent scholars tend to cite less than their lesser colleagues. Whether it is because in virtue of their eminence they are at the center of the information network of their discipline and thus only cite the most important work, or whether it is because they believe that their eminence will serve in lieu of references to convince the reader of the validity of their claims remains unclear.
Third, the role of article quality seems to be a crucial determinant of whether a scientific contribution is used. The significance of this variable for both areas provides strong support for the normative view of science in which citations reflect intellectual debts based on the relative worth of a given contribution (Hagstrom 1965; Kaplan 1965;
Merton 1973). However, the size of the coefficient for article quality and its relative
importance seems to differ across areas. The coefficient for cited article quality is about
three times as large in rational expectations than in celestial masers. Furthermore, while
cited article quality is the single most important predictor of the likelihood of a citation in
rational expectations, it is only sixth best in celestial masers.
128 Fourth, authors are more likely to cite their own work than the work of others.
This makes sense, especially since authors usually work in an area in which they constantly build upon their own previous contributions by modifying, refining, and expanding them. However, the effect of citing one's own work appears stronger in rational expectations than in celestial masers (the coefficient is twice as large in the former area), possibly because the work of economists is more dependent upon pursuing a similar line of argumentation across one's own papers while that of astrophysicists may be more dependent upon actual new discoveries in their field. An economist may be able to keep citing his or her own previous work because he or she keeps making the same argument across papers, while an astrophysicists may have to adapt his or her research to new technical developments in the field, thus reducing the possibility of recycling the same old argument.
Finally, literature aging, as indicated by the time elapsed between the potentially citing and the potentially cited articles, significantly decreases the likelihood that a contribution is used. This finding goes against existing arguments on the nature of knowledge across the hard and soft sciences (Bazerman 1988; Griffith and Small 1983;
Price 1965, 1970), and goes against the first hypothesis on disciplinary differences 1 developed in chapter 2. Traditionally, sociologists and historians of science have argued that knowledge in the natural sciences is much more cumulative than in the social sciences and this should result in hard scientists citing a more recent literature than social scientists. My results contradict this notion, at least as it applies to the two research areas studied in this dissertation. Literature aging seems to be as important in reducing the
129 likelihood of a citation in economics as it is in astrophysics. This finding is partly explained by the fact that the zero-order relationship between article quality and publication date is much stronger in rational expectations than in celestial masers (r=-.47, p<.000 vs. r=.08, p=.38), indicating that the high quality articles in economics were largely published at the beginning of the development of the area. Thus, controlling for article quality suppresses the negative effect of the article's age on subsequent citation.
However, it is also possible that the distinction regarding literature aging does not follow disciplinary differences along a hard-soft dimension, but instead has more to do with the type of research conducted. By all accounts (e.g., Attfield et al. 1985; Miller
1994; Shaw 1984) rational expectations was a highly progressive research area in which participants believed they were solving important issues in macro-economics. Thus it may have been a "hot" research area quite untypical of "everyday" research in the social sciences."* This explanation suggests that literature aging may vary on other dimensions than the hard-soft one, such as whether a research area is considered progressive (i.e.,
"hot") or stagnant (i.e., "cold").
Until now I have focused on the similarities across the two areas. However, my second hypothesis based on the literature about the relationship between consensus and the reward system of science across areas (Cole 1979; Hargens 1988; Whitley 1984;
Zuckerman and Merton 1971, 1972) suggests that as a result of lower consensus over
“’After all, rational expectations was given the official "seal of approval" of being a successful research area when its main proponent (R. Lucas) was awarded a Nobel Prize, an honor bestowed only on very few research areas in economics.
130 what constitutes quality work in the social sciences, once controlling for the cognitive content of the cited article, functionally irrelevant characteristics of the cited author should continue to have a significant positive effect on the presence of a citation among two articles. The results from Table 6.3 above tend to support this hypothesis.
Specifically, while functionally irrelevant characteristics of the cited author have no effect in astrophysics, both the institutional prestige and the seniority of the cited author affect the likelihood that a citation will be made in economics. This finding is also consistent with a social constructivist interpretation of the citation process in which authors ally themselves with the most prestigious scholars in the field. However, the fact that the institutional prestige of the cited author is significant only for the social science area suggests that this finding has more to do with disciplinary differences in consensus than with a universal view of the citation process as merely reproducing the stratification structure of one's discipline by enlisting the prestige of the most famous authors through citing their work.
The results from the regression analyses also suggest other non-hypothesized similarities as well as differences across the two areas. First, both areas provide evidence that characteristics of the potentially citing article as well as relational variables are significant determinants of citations in addition to the cited article characteristics.
Specifically, while identifying different variables, the results from both areas indicate that the content of the citing article (i.e., whether it is mostly theoretical, its number of quantitative tables and graphs per page, whether it is a book chapter) affects what it cites.
131 The content of the cited article, however, seems to be more important in rational expectations than in celestial masers. While the vast majority of the cited article characteristics have statistically significant effects on the presence of a citation in rational expectations, almost none of them do in celestial masers, except for the article's quality.
It appears that, for astrophysics, it is the content of a cited articlein relation to the citing article that matters (wimess the significant effects of the relational variables indicating article content), while in economics it may just be the content of the cited article, regardless of its relation to the citing article. This explanation is consistent with the estimated effects of whether the citing and the cited article study the same topic and whether they both claim to make a theoretical contribution being significant only for the celestial masers area. Furthermore, the lack of effect of potentially citing and cited articles studying the same subtopic for economics is also consistent with Cozzens (1985) and Bazerman (1988) who found that social scientists tended to cite general concepts while natural scientists mostly referred to a specific knowledge claim (e.g., discovery, specific technique) in the cited article. This argument suggests that what the citing article cites is much more topic-dependent in the natural than in the social sciences.
Finally, the results firom both areas offer no evidence that the presence of social ties among the citing and the cited authors, as either graduates of the same institution or as colleagues, increases the likelihood that a citation will be made between two articles.
This finding does not support the long-standing belief that scientists might overcite the work of their institutional colleagues. However, it is possible that the effect of social ties
132 on citations operates largely outside of one's research area or through more direct ties such as student-mentors or co-authors.
Conclusion
In this chapter I compared the findings from the analyses of celestial masers and rational expectations articles, and discussed their meanings in terms of existing arguments about the differences in the organization and practice of science in the hard and soft fields. While the results regarding the nature of the stratification system across fields were consistent with past arguments, those regarding the diffusion and incorporation of knowledge in the natural and the social sciences were not.
The results from both areas seem to support claim that, possibly as a result of lower consensus over what constitutes quality work (Hargens 1988; Lodahl and Gordon
1972; Pfeffer 1993; Whitley 1984) as well as lower codification of knowledge
(Zuckerman and Merton 1971,1972), or lack of paradigm development (Kuhn 1970), scholars working in the social sciences rely more on "who one is," as indicated by functionally irrelevant author characteristics, than scholars working in the natural sciences. Institutional prestige and seniority were significant only in the rational expectation area, suggesting that social scientists rely not only on the quality of an article when using a scientific contribution, but also on the prestige of its author.
These results could also be seen as providing evidence for the argument that citations are used as tools for persuasion (Gilbert 1977) and that citation flows tend to reflect existing patterns of institutional stratification (Latour 1987). However, the fact
133 that institutional prestige was significant in only one area suggests that the mechanism of allocation of citations is more dependent upon disciplinary differences in consensus and codification than upon the authors' desire to ally themselves with the most prestigious authors in their field. Indeed, if the social constructivist argument were correct, we would expect the role of functionally irrelevant cited author characteristics to be significant across disciplines, regardless of their position along a hard-soft dimension.
The results from the two areas also failed to support Price’s (1965) and others'
(Bazerman 1988; Griffith and Small 1983) arguments regarding the structure of scientific literatures in the hard and soft sciences. The positive and significant effect of years elapsed between articles for both areas indicates no difference in the extent to which natural and social science literatures incorporate new knowledge and cumulatively build upon recent work. Although this finding is partly the result of a difference in the relationship between article quality and date of publication across the two areas, it is also possible that literature aging is not related to disciplinary differences along a hard-soft dimension but rather varies according to other characteristics of the research area such as whether it is perceived as progressive (i.e., a "hot" research area) or instead as stagnant
(i.e., a "cold" research area).
Finally, the comparison of the celestial masers and rational expectations data suggested the possible existence of common determinants of citations across scientific fields. Those were the length of the citing article, the eminence of the citing author, the quality of the cited article, the overcitation of an author's own work, and the time elapsed between citing and cited articles.
134 CHAPTER?
CONCLUSION
Over the years citations have increasingly been used as a tool for the assessment of academic performance. Partly on the basis of citations, individual scientists are promoted, departments are eliminated, and research areas receive funding over others.
Thus, citations are a critical micro-level stratifying mechanism that help determine the location of individual scientists or that o f entire research areas in the overall stratification structure of contemporary science. Despite the documented importance of citations, few studies have attempted to explain the factors that influence whether a given paper, and therefore a given scholar, is cited. The goal of this dissertation has been to advance our understanding of the citation process by identifying the determinants of citations in two research areas. In this chapter I review the main findings of this research and discuss their limitations as well as implications for future studies.
This research was based on the central argument that the citation process involves a dyadic relationship between a potentially citing paper and a potentially cited paper.
Although this may sound like a fairly obvious argument, past research on the determinants of citations (e.g., Shadish et al. 1995; Stewart 1983, 1990) failed to incorporate characteristics of the citing articles into models of the citation process. To
135 assess the role of characteristics of potentially citing articles on the existence of citations I used a network analytic approach that traced all the possible citation links between later papers and earlier papers in the two research areas mentioned above. Adopting a comparative approach allowed me to test arguments that the reward system differs across the hard/soft disciplinary divide, possibly as a result of differences in levels of codification and consensus over what constitutes quality work (Hagstrom 1965; Hargens
1975; Storer 1967; Whitley 1984; Zuckerman and Merton 1971,1972).
The first research question I raised was the extent to which characteristics of the potentially citing article and its corresponding author(s) and journal influence the presence of a citation between two papers. The results from both the celestial masers and
rational expectations areas indicated that these characteristics of citing papers have
statistically significant effects on the presence of citations. In the field of astrophysics,
citing article characteristics such as size, number of contributing authors, and recency of
knowledge cited all significantly increased the likelihood of the existence of a citation,
while the eminence of the citing author and the article's quantitative content (i.e., the
number of quantitative tables and graphs per page) decreased it. For economics, the size
of the citing article as well as publication in an edited volume also increased the
likelihood that a citation existed, while theoretical content and the eminence of the citing
author decreased it. These findings emphasize the need to conceptualize citations as a
dyadic relationship between a potentially citing and a potentially cited paper in which
characteristics of both help account for the presence of a citation. In addition, these
136 results suggest that in order to be highly cited, scientists should know authors of little eminence who tend to write long papers.
Furthermore, the importance of including characteristics of potentially citing articles is also evidenced by the key role played by relational variables as determinants of citations. Results from the analyses of both areas documented effects of variables indicating various relationships between citing and cited articles and authors such as similar theoretical or topical content, time elapsed between publication, publication in the same journal, or whether the citing and the cited articles were written by the same author(s). These relational variables can only be captured by treating citations as a network of citation flows.
The second research question investigated by this dissertation addressed the
relative importance of article, author and journal characteristics of both the potentially
citing and potentially cited papers on the presence of a citation. This question was
motivated by long-standing and competing arguments as to why scientists cite previous
work. Normative accounts (Hagstrom 1965; Kaplan 1965; Merton 1973), by
emphasizing the repayment of intellectual debt, suggest that what one writes, as indicated
by the quality and content of articles, is the main determinant of citations. By contrast,
social constructivist accounts (Gilbert 1977; Latour 1987) emphasize the role of citations
as tools of persuasion and suggest that who one is, as indicated by functionally irrelevant
cited author characteristics, is an important — if not the main — determinant of citations.
My results provided only weak support for the social constructivist argument.
Cited author characteristics played no part in explaining the presence of a citation in
137 celestial masers, and only had a weak, although significant effect in rational expectations.' In contrast, cited article characteristics were the strongest and most numerous determinants of the presence of a citation in both research areas. Specifically, the significant positive effect of article quality provided strong support for the normative argument that citations are repayments of intellectual debts. However, it must pointed out that social constructivists could interpret my measure of article quality as merely reflecting an article's "visibility." In this sense then, the positive effect of article quality could be seen as reflecting an attempt on the part of authors to enhance the persuasiveness of their paper by citing the most "visible" articles. This alternative interpretation of the effect of article quality is nevertheless not quite as compelling as that offered by the normativists since one could argue, as the social constructivist actually do, tnat scientists are more likely to be persuaded by the visibility of an author's characteristics, such as institutional prestige or eminence, than by the quality of his or her contribution, especially given the low level of consensus over the worth of articles in the social sciences, as indicated by the commonly high journal rejection rates and low level of inter-referees agreement.
The third research question 1 investigated was whether the existence of social ties among authors, as colleagues or students trained at the same institution, influenced their citing decisions. The answer is negative. Although in both areas the effect is in the predicted direction, it fails to ever reach significance. Thus, my data provide little
'Cited author institutional prestige was only the seventh best predictor of the presence of a citation link in the economics area.
138 evidence that, as some argued (e.g.. Edge 1977; Kaplan 1965), scholars overcite the work of their institutional colleagues.
The final research question raised by this dissertation was the extent to which the determinants of citations differ among the natural and the social sciences. While my results supported past arguments regarding the nature of the stratification system across fields, possibly as a result of differences in consensus and codification (Hagstrom 1965;
Hargens 1975; Pfeffer 1993; Whitley 1984; Zuckerman and Merton 1971,1972), they failed to support arguments regarding differences in the incorporation of knowledge across the natural and social sciences (Bazerman 1988; Griffith and Small 1983; Price
1965, 1970).
Consistent with much of the literature hypothesizing differences in the reward system of disciplines variously located along a hard-soft dimension as a result of greater consensus over what constitutes quality work in the natural than the social sciences, my results suggested that the reward system is more universalistic and less dependent upon
"who one is" in celestial masers than in rational expectations. Specifically, I found significant positive effects of institutional prestige only for the economics area. Since the estimated model nets out the effect of article quality, we must conclude that social scientists at least partially rely on the prestige of an author when citing.
Results fi’om both areas provided no support for the hypothesis that the pattern of literature aging differs along a hard/soft dimension. Price (1965,1970) argued that natural science research is characterized by a pattern of rapid incorporation of new knowledge and cumulative building upon recent work that manifests itself by the rapid
139 obsolescence of scientific contributions. In contrast. Price claimed that social science research follows an archival pattern in which scholars proceed unaffected by the aging of their discipline's literature, equally citing old classics and new contributions. My results document no such difference. Literature aging had a significant negative effect on the presence of a citation in both areas. Although this finding is partly explained by the strong negative relationship between article quality and publication date only in economics, it is also possible that literature aging may not be related to disciplinary differences along a hard-soft dimension, but instead varies along other characteristics of research areas such as whether it is progressive or not.
The comparison of the two areas also identified several determinants of citations common to both. Those were the length of the citing article, the eminence of the citing author, the quality of the cited article, the overcitation of an author’s own work, and the time elapsed between citing and cited articles. However, the effects of cited article quality and citation of one's own previous work were stronger in rational expectations than in celestial masers, suggesting that quality may be more important in economics and that the cumulative patterns of building upon one's own past contributions might differ across areas.
Limitations of the Research
Although I tried to be as accurate as possible in constructing this study, there nevertheless remain a few limitations. First, the analyses were limited by the availability of the data in several ways. The lack of prestige scores for universities outside of the
140 United States as well as for most non-academic institutions limited the comprehensiveness of the prestige measures of Ph.D. and employing institutions. This was more of a problem in the celestial masers area where a fairly large number of authors
(i.e., 28.75 percent) were from foreign countries. Thus it is possible that a better measure of employment prestige may have had a different effect on the presence of a citation in the celestial masers area. Furthermore, the lack of identical measures of institutional prestige across areas suggests that we should use caution when interpreting the disciplinary differences associated with this variable. Although I replicated the celestial masers analyses using institutional prestige measures comparable to the ones used in rational expectations and the results remained similar to the analyses using different indicators of prestige, the prestige scores for economics and astrophysics were obtained at a different time (1982 vs. 1993), and this could have affected the overall conclusions, possibly by over- or underestimating the effect of institutional prestige.
This research was also limited by the types of social ties I was able to collect information on. In particular, because this information is not readily available in standard sources or directories, I was unable to uncover student-mentor ties as well as ties among authors who had co-authored papers outside of the areas studied. One would suspect close ties, such as student-mentor, to have a stronger influence on subsequent citations than loose ties such as merely having received one's Ph.D. from the same institution, partly because students are likely to work in the same subarea as that of their mentors, while they may have little contact with other graduate students in their program. For example, in a study of the role of social ties on citations in a sociological research
141 specialty. Yuan (1994) found co-author relationships to have the strongest zero-order correlation with subsequent citations, followed by teacher-student relationships, and colleagueships, in that order — although all of these effects were nevertheless rather weak.
Thus it is possible that a better measure of social ties might have uncovered a significant positive effect of such ties on subsequent citations.
Furthermore, recent research (Geison and Holmes 1993) has underscored the importance of research schools for the development of science. It is plausible that scientists who belong to the same "school," as indicated by their "...pursuing a reasonably coherent program of research side-by-side with advanced students in the same institutional context and engaging in direct, continuous social and intellectual interaction"
(Geison 1981:23), are also more likely to cite one another's work. However, because of the lack of available indicators of "direct, continuous social and intellectual interaction," 1 was imable to estimate the effect of belonging to the same research school on the presence of a citation between two articles.
This dissertation is also unable to address the issue of how representative the two research areas chosen for analyses are of (1) their larger disciplines, and (2) research areas in the natural and the social sciences. We know that the two research areas chosen distinguish themselves by the fact that their participants generally believed they had made substantial progress in advancing the understanding of their subject matter. It is possible that the determinants of citations differ based on the level of acknowledged progressiveness of a research area. For example, author characteristics may be more consequential in a stagnant area where most authors believe the worth of most
142 contributions to be of relatively little significance. In addition, as mentioned earlier, it is possible that progressiveness is also a key dimension in understanding the role of literature aging across areas. These issue cannot be addressed without new research on areas exhibiting different levels of progressiveness.
Finally, the results of the analyses are limited by the inability of existing statistical software to simultaneously correct for the presence of correlated disturbances and heteroskedasticity. Although I took every step possible to triangulate the analyses by comparing results from different estimating techniques (EGLS versus logistic) and obtaining similar results, it is possible that the estimated effects of the independent variables might have been slightly different had I been able to correct for both problems at once.
Contributions of the Research
The contributions of this dissertation were both substantive and methodological.
At the substantive level my research extended the literature on the determinants of citations by identifying factors that influence citations in the natural and the social sciences, thus clarifying the allocation of credit at the micro-level of the citation. By conceptualizing citations as dyadic relationships between a potentially citing and a potentially cited paper, I showed the importance of including characteristics of citing papers in understanding citation flows. Furthermore, my design seriously improved upon earlier studies by enabling me to include a measure of article quality in order to test long standing and competing arguments regarding the use and functions of citations.
143 By adopting a comparative perspective, the design of this dissertation also allowed me to test hypotheses regarding (1) the cumulative nature of knowledge in the natural sciences than in the social sciences and (2) the extent to which the allocation of rewards is similar or different across research areas differentially located along a hard/soft dimension.
At the methodological level this dissertation developed a new approach to the study of citations by applying techniques imported from studies of social networks. By treating citations as a network of citation flows between a set of potentially citing and potentially cited papers, I showed how researchers can simultaneously estimate the effects of characteristics of citing and cited articles as well as the effects of various relational variables on the presence of a citation. This approach improves upon past studies which have relied solely on cited article characteristics to explain the determinants of citations.
Suggestions for Future Research and Implications
The results of this dissertation lend themselves to making several suggestions for future research. The scope of the analyses has been restricted to two areas, and as a result prevents the generalization of the findings to other scientific disciplines in the natural and the social sciences. More work is needed that will extend the present analyses to other areas of research variously located along the hard-soft dimension in order to answer whether the determinants of citations are specific to the characteristics of their research areas, or whether there are universal determinants of citations that hold across scientific disciplines. Specifically, we still do not know whether scientists working in "hot"
144 research areas exhibit different citation patterns than their colleagues working in areas deemed "cold."
A finding that went against expectations was the lack of an effect of the presence of social ties among citing and cited authors on the presence of a citation. As mentioned earlier, this finding may partially reflect the fact that these ties are not the strongest one could possibly assess. Future studies should attempt to better assess the effects of various social relationships among authors by collecting more detailed information on the types of ties existing (such as student-mentor ties, co-authors outside of the research area studied, or belonging to the same research school).
Finally, in light of the growing importance played by citations in contemporary science, it is my hope that scholars will use these and other results fi-om empirical studies of the determinants of citations to start developing a body of testable hypotheses that together could form the core of a theory of citing.
The results of this dissertation also suggest a few implications. The consistent effect of characteristics of citing articles on citations should alert future researchers to the need to systematically conceptualize citations as dyadic relationships. One of the main contributions of this dissertation has been to document the need to treat citations as being determined both by a citer and a citee, where the meaning of the cited article is imparted by the citing author (Small 1978).
As a closing word, I would like to caution administrators and funding agencies to be critical in their use of citation counts when making decisions affecting the lives of individual scientists as well as the existence of departments or the development of
145 specific research areas. Although citation counts may be the best available measure of the quality of a scientist's contribution, as this research showed the determinants of citations have a multitude of explanations, and assuming that citations merely reflect the quality of one's work ignores the role played by citers in determining what, and eventually who, gets cited.
146 REFERENCES
Abt, H. A. 1981. "Long-Term Citation Histories of Astronomical Papers." Publications o f the Astronomical Society o f the Pacific 93:207-10.
------. 1993. "Institutional Productivities."Publications o f the Astronomical Society o f the Pacific 105:794-98.
------1995. "Changing Sources of Published Information." Publications o f the Astronomical Society o f the Pacific 107:401-03.
Aldrich, J. H., and P. D. Nelson. 1984. Linear Probability, Logit, and Probit Models. Beverly Hills, CA: Sage.
Allen, M. P. 1990."The 'Quality' of Journals in Sociology Reconsidered: Objective Measures of Journal Influence."ASA Footnotes 18:4-5.
Allison, P. D., and J. S. Long. 1990. "Departmental Effects on Scientific Productivity." American Sociological Review 55:469-78.
American Astronomical Society. 1995-1996. Membership Directory.
American Economic Association. 1974-1993. Directory o f Members.
American Men and Women o f Science. 1989. New York: Bowker.
American Men and Women o f Science: Economics. 1974. New York: Bowker.
Attfield, C. L. P., D. Demery, and N. W. Duck. 1985. Rational Expectations in Macroeconomics: An Introduction to Theory and Evidence. Oxford: Basil Blackwell.
Baldi, S. 1994. "Changes in the Stratification Structure of Sociology 1964-1992."The American Sociologist 25:28-44.
------1995. "Prestige Determinants of First Academic Job for New Sociology Ph.D.s 1985-1992." The Sociological Quarterly 26:111
147 Baldi, S., and L. L. Hargens. 1995. "Reassessing the N-Rays Reference Network: The Role of Self Citations and Negative Citations." Scientometrics 34:239-53.
------1997. "Reexamining Price's Conjectures on the Structure of Reference Networks: Results from the Special Relativity, Spatial Diffusion Modeling, and Role Analysis Literatures." Social Studies o f Science (Forthcoming).
Barber, B. 1952. Science and the Social Order. Glencoe, IL: Free Press.
Barrett, A. H., P. R. Schwartz, and J. W. Waters. 1971. "Detection of Methyl Alcohol in Orion at a Wavelength of ~1 Centimeter. The Astrophysical Journal (Letters) 168:L101-L106.
Bayer, A. E., and J. F. Folger. 1966. "Some Correlates of a Citation Measure of Productivity in Science." Sociology o f Education 39:381-89.
Bazerman, C. 1988. Shaping Written Knowledge: The Genre and Activity o f the Experimental Article in Science. Madison: University of Wisconsin Press.
Belsley, D., E. Kuh, and R. Welsch. 1980. Regression Diagnostics: Identifying Influential Data and Sources o f Collinearity. New York: Wiley.
Blank, R. M. 1991. "The Effects of Double-Blind versus Single-Blind Reviewing: Experimental Evidence from the American Economic Review." American Economic Review 81:1041-67.
Bloor, D. 1976. Knowledge and Social Imagery. London: Routledge & K. Paul.
Bott, D. M., and L. L. Hargens. 1991. "Are Sociologists' Publications Uncited? Citation Rates of Journal Articles, Chapters and Books." The American Sociologist 22:147-58.
Breiger, R. L. 1976. "Career Attributes and Network Structure: A Blockmodel Study of a Biomedical Research Specialty." American Sociological Review 41:117-35.
Cabell, D. W. E., ed. 1988. Cabell’s Directory o f Publishing Opportunities in Business and Economics, 4th ed. Beaumont, TX: Cabell.
Cartter, A. M. 1966. An Assessment o f Quality in Graduate Education. Washington, DC: American Council on Education.
Cassidy, J. 1996. "The Decline of Economics." The New Yorker (December 2): 50-60.
148 Cheung, A. C., D. M. Rank, C. H. Townes, D. D. Thornton, and W. J. Welch. 1969. "Detection of Water in Interstellar Regions by its Microwave Radiation." Nature 221:626-28.
Chubin, D. E., and S. D. Moitra. 1975. "Content Analysis of References: Adjunct or Alternative to Citation Counting?" Social Studies o f Science 5:423-41.
Cicchetti, D. V. 1991. "The Reliability of Peer Review for Manuscript and Grant Submissions: A Cross-Disciplinary Investigation." Behavioral and Brain Sciences 14:119-34.
Clark, K. E. 1957. America's Psychologists: A Survey o f a Growing Profession. Washington, DC: American Psychological Association.
Cleverdon, C. W., and J. S. Kidd. 1976. "Redundancy, Relevance, and Value to the User in the Outputs of Information Retrieval Systems."Journal o f Documentation 32:159-73.
Cohen, J., and P. Cohen. 1975. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Hillsdale, NJ: Wiley & Sons.
Cole, J. R., and S. Cole. 1973. Social Stratification in Science. Chicago: University of Chicago Press.
Cole, S. 1979. "Age and Scientific Performance." American Journal o f Sociology 84:958- 77.
------1992. Making Science: Between Nature and Society. Cambridge, MA: Harvard University Press.
Cole, S., J. R. Cole, and L. Dietrich. 1978. "Measuring the Cognitive State of Scientific Disciplines." In Y. Elkana, J. Lederberg, R. K. Merton, A. Thackray, and H. A. Zuckerman (Eds.), Toward a Metric o f Science, pp. 209-51. New York: Wiley.
Collins, R. 1975. Confiict Sociology: Toward an Explanatory Science. New York: Academic Press.
Cook, A. H. 1977. Celestial Masers. Cambridge: Cambridge University Press.
Cozzens, S. E. 1985. "Comparing the Sciences: Citation Context Analysis of Papers from Neuropharmacology and the Sociology of Science." Social Studies o f Science 15:127-53.
149 1989. "What Do Citations Count? The Rhetoric-First Model." Scientometrics 15:437-44.
Crane, D. 1965. "Scientists at Major and Minor Universities: A Study of Productivity and Recognition." American Sociological Review 30:699-713.
------1972. Invisible Colleges: Diffusion o f Knowledge in Scientific Communities. Chicago: University of Chicago Press.
Crane, D., and H. Small. 1992. "American Sociology since the Seventies: The Emerging Identity Crisis in the Discipline." In T. C. Halliday and M. Janowitz (Eds.), Sociology and Its Publics: The Forms and Fates of Disciplinary Organization, pp. 197-234. Chicago: University of Chicago Press.
Cronin, B. 1984. The Citation Process: The Role and Significance o f Citations in Scientific Communication. London: Taylor Graham.
Directory o f Physics and Astronomy Faculties in North American Colleges and Universities. 1970. New York: American Institute of Physics.
Directory o f Physics and Astronomy Staff members. 1976. New York: American Institute of Physics.
Earle, P., and B. Vickery. 1969. "Social Science Literature Use in the U.K. as Indicated by Citations." Journal o f Documentation 25:123-41.
Edge, D. 1977. "Why I Am Not a Co-citationist." Society for Social Studies o f Science Newsletter 2:13-9.
Eichner, A. S., ed. 1983. Why Economics is not yet a Science. Armonk, NY : M. E. Sharpe.
Elitzur, M. 1995. "Masers in the Sky." Scientific American, February:68-73.
Fischer, S. 1977. "Long Term Contracting, Sticky Prices, and Monetary Policy: A Comment." Journal o f Monetary Economics 3:317-23.
Finney, B. 1979. The Reference Characteristics o f Scientific Texts. London: City University, Centre for Information Science (Msc Thesis).
Fox, M. F. 1987. "Gender, Environmental Milieu, and Productivity in Science." In J. T. Bruer, J. R. Cole, and H. Zuckerman (Eds.), Women and the Pursuit o f Science. New York: Norton.
150 Frost, C. 1979. "The Use of Citations in Literary Research: Preliminary Classification of Citation Functions." Library Quarterly 49:399-414.
Fuchs, S. 1992. The Professional Quest for Truth: A Social Theory o f Science and Knowledge. Albany, NY: State University of New York Press.
Gale Directory o f Publications. 1988. Detroit: Gale Research Company.
Garvey, W. D. 1979. Communication, the Essence o f Science: Facilitating Information Exchange among Librarians, Scientists, Engineers, and Students. New York: Pergamon Press.
Gaston, J. 1978. The Reward System in British and American Science. New York: Wiley- Interscience.
Geison, G.L. 1981. "Scientific Change, Emerging Specialties, and Research Schools." History o f Science 9:20-40.
Geison, G. L., and F. L. Holmes, eds. 1993. Research Schools: Historical Reappraisals. Philadelphia: History of Science Society.
Gieryn, T. F. 1995. "Boundaries of Science." In S. Jasanoff, G. E. Markle, J. C. Petersen, and T. Pinch (Eds.), Handbook o f Science and Technology Studies, pp. 393-443. Thousand Oaks, CA: Sage.
Gilbert, G. N. 1977. "Referencing as Persuasion." Social Studies of Science 7:112-22.
Glenn, N. D. 1971. "American Sociologists' Evaluations of Sixty-Three Journals." The American Sociologist 6:298-303.
Goldberger, A. S. 1964. Econometric Theory. New York: Wiley.
Goldberger, M. L., B. A. Maher, and P. E. Flattau. 1995. Research-Doctorate Programs in the United States: Continuity and Change. Washington, DC: National Academy Press.
Gordon, R. A. 1968. "Issues in Multiple Regression."American Journal o f Sociology 73:592-616.
Greene, W. H. 1993. Econometric Analysis, 2d ed. Englewood Cliffs, NJ: Prentice Hall.
151 Griffith, B. C., and H. G. Small. 1983. "The Structure of the Social and Behavioral Sciences Literature." In S. Schwartz (Ed.), Stockholm Papers in Library and Information Science, pp. 1-53. Stockholm: Royal Institute of Technical Libraries.
Griffith, B. C., H. G. Small, J. A. Stonehill, and S. Dey. 1974. "The Structure of Scientific Literature II: Toward a Macro- and Microstructure for Science."Science Studies 4:339-64.
Hagstrom, W. O. 1965. The Scientific Community. New York: Basic Books.
Hanushek, E. A., and J. Jackson. 1977. Statistical Methods for Social Scientists. New York: Academic Press.
Hargens, L. L. 1969. "Patterns of Mobility of New Ph.D.'s Among American Academic Institutions." Sociology o f Education 42:18-37.
------1975. Patterns o f Scientific Research: A Comparative Analysis o f Research Work in Three Scientific Fields. Washington, DC: ASA Rose Monograph Series.
------1988. "Scholarly Consensus and Journal Rejection Rates." American Sociological Review 3:139-51.
------1993. "Reference Networks and Scientific Development: A Comparative Study." NSF Proposal, Grant # SBR-9223317.
Hargens, L. L., and W. O. Hagstrom. 1982. "Scientific Consensus and Academic Status Attainment Patterns." Sociology o f Education 55:183-96.
Hargens, L. L., and L. Kelly-Wilson. 1994. "Determinants of Disciplinary Discontent." Social Forces 72:1177-95.
Hargens, L. L., and H. Schuman. 1990. "Citation Counts and Social Comparisons: Scientists’ Use and Evaluation of Citation Index Data." Social Science Research 19:205-21.
Hodges, T. 1978. Citation Indexing: Its Potential for Bibliographic Control. Berkeley: University of California (Ph.D. Thesis).
Holtz, J. Z. 1968. "Energy Requirement and Mechanisms for OH Galactic Masers." The Astrophysical Journal (Letters) 153 :L 117-L121.
International Physics and Astronomy Directory. 1969. New York: W. A. Benjamin.
152 Irregular Serials and Annuals. 1982. New York: Bowker.
Jones, L. V., G. Lindzey, and P. Coggeshall, eds. 1982. An Assessment o f Research- Doctorate Program in the United States: Social and Behavioral Sciences. Washington, DC: National Academy Press.
Kaplan, N. 1965. "The Norms of Citation Behavior: Prolegomena to the Footnote." American Documentation 16:179-84.
Keith, B., and N. Babchuk. 1994. "A Longitudinal Assessment of Productivity in Prominent Sociology Journals and Departmental Prestige." The American Sociologist 25:4-27.
Knorr-Cetina, K. 1981. The Manufacture o f Knowledge: An Essay on the Constructivist and Contextual Nature o f Science. Oxford: Pergamon Press.
------1995. "Laboratory Studies: The Cultural Approach to the Study of Science." In S. Jasanoff, G. E. Markle, J. C. Petersen, and T. Pinch (Eds.), Handbook o f Science and Technology Studies, pp. 140-66. Thousand Oaks, CA: Sage.
Kuhn, T. S. [1962] 1970. The Structure o f Scientific Revolutions, 2nd ed. Chicago: University of Chicago Press.
Latour, B. 1987. Science in Action: How to Follow Scientists and Engineers Through Society. Cambridge: Harvard University Press.
Latour, B., and S. Woolgar. 1979. Laboratory Life: The Social Construction o f Scientific Facts. Beverly Hills: Sage.
Leontief, W. 1983. "Foreword." In A. S. Eichner (Ed.), Why Economics is not Yet a Science, pp. vii-xi. Armonk, NY: M. E. Sharpe.
Liebert, R. J. 1976. "Productivity, Favor, and Grants among Scholars." American Journal o f Sociology 82:664-73.
Lindsey, D., and T. Lindsey. 1978. "The Outlook of Journal Editors and Referees on the Normative Criteria of Scientific Craftmanship: Viewpoints from Psychology, Social Work and Sociology." Quality and Quantity 12:45-62.
Line, M. B. 1979. "The Influence of the Type of Sources Used on the Results of Citation Analyses." Journal o f Documentation 35:265-84.
153 1981. "The Structure of Social Science Literature as Shown by a Large-Scale Citation Analysis." Social Science Information Studies 1:67-87.
Lipetz, B. A. 1962. "Compilation of an Experimental Citation Index from Scientific Literature." American Documentation 13:251-66.
1965. "Improvement of the Selectivity of Citation Indexes to Science Literature Through Inclusion of Citation Relationship Indicators."American Documentation 16:81-90.
Little, R. J. A., and D. B. Rubin. 1990. "The Analysis of Social Science Data with Missing Values." Sociological Methods and Research 18:292-326.
Lodahl, J. B., and G. G. Gordon. 1972. "The Structure of Scientific Fields and the Functioning of University Graduate Departments."American Sociological Review 37:57-72.
Long, J. S. 1978. "Productivity and Academic Position in the Scientific Career." American Sociological Review 43:889-908.
------1990. "The Origins of Sex Differences in Science." Social Forces 68:1297- 1315.
Long, J. S., P. D. Allison, and R. McGinnis. 1979. "Entrance into the Academic Career." American Sociological Review 44:816-30.
Lucas, R. E., Jr. 1972. "Expectations and the Neutrality of Money." Journal o f Economic Theory
------. 1976. "Econometric Policy Evaluation: A Critique." In K. Brunner and A. H. Meltzer (Eds.), The Phillips Curve and Labor Markets, pp. 19-46. Amsterdam: North-Holland.
Maddock, R., and M. Carter, 1982. "A Child's Guide to Rational Expectations." Journal o f Economic Literature 20:39-51.
McCain, K. 1984. "Longitudinal Author Cocitation Mapping: The Changing Structure of Macroeconomics." Journal o f the American Society for Information Science 36:351-9.
Merton, R. K. 1942. "Science and Technology in a Democratic Order." Journal o f Legal and Political Sociology 1:115-26.
154 1957. "Priorities in Scientific Discoveries." American Sociological Review 22:635-59.
1968. "The Matthew Effect in Science." Science 159:56-63.
1973. The Sociology o f Science: Theoretical and Empirical Investigations. Chicago: University of Chicago Press.
Mezger, P. G., W. Altenhoff, J. SchramI, B. F. Burke, E. C. Reifenstein III, and T. L. Wilson. 1967. "A New Class of Compact H „ Regions Associated with OH Emission Sources." The AstrophysicalJournal 150:L157-L166.
Miller, P. J., ed. 1994. The Rational Expectations Revolution: Readings from the Front Line. Cambridge, MA; MIT Press.
Moran, J. M., B. F. Burke, A. H. Barrett, A. E. E. Rogers, J. A. Ball, J. C. Carter, and D. D. Cudaback. 1968. "The Structure of the OH Source in W3." The Astrophysical Journal (Letters) 152:L97-L101.
Moravcsik, M. J., and P. Murugesan. 1975. "Some Results on the Function and Quality of Citations." Social Studies o f Science 5:86-92.
Morgan, T. 1988. "Theory versus Empiricism in Academic Economics: Update and Comparisons." Journal o f Economic Perspectives 2:159-64.
Moser, C. A., and G. Kalton. 1971. Survey Methods in Social Investigation, 2d ed. London: Heinemann Educational.
Mulkay, M. J. 1979. Science and the Sociology o f Knowledge. London: Allen & Unwin.
Mullineaux, D. J. 1978. "On Testing for Rationality: Another Look at the Livingston Price Expectations Data." Journal o f Political Economy 86:329-36.
Mullins, N. C., L. L. Hargens, P. K. Hecht, and E. L. Kick. 1977. "The Group Structures of Two Cocitation Clusters: A Comparative Study."American Sociological Review 42:552-62.
Muth, J. 1961. "Rational Expectations and the Theory of Price Movements." Econometrica 29:315-35.
Nagel, E. 1961. The Structure o f Science: Problems in the Logic o f Scientific Explanation. New York: Harcourt, Brace & World.
155 Nunnally, J. C., and I. H. Bernstein. 1994. Psychometric Theory, 3d ed. New York: McGraw-Hill.
Oppenheim, C., and S. P. Renn. 1978. "Highly Cited Old Papers and the Reasons Why They Continue to Be Cited." Journal o f the American Society for Information Science 29:225-21.
Pfeffer, J. 1993. "Barriers to the Advance of Organizational Science: Paradigm Development as a Dependent Variable." Academy of Management Review 18:599- 620.
Pfeffer, J., A. Leong, and K. Strehl. 1977. "Paradigm Development and Particularism: Journal Publication in Three Scientific Disciplines."Social Forces 55:938-51.
Popper, K. [1934] 1959.The Logic o f Scientific Discovery. New York: Harper.
------1963.Conjectures and Refutations. New York: Harper.
Price, D. J. De Solla. 1963. Little Science, Big Science. New York: Columbia University Press.
------1965. "Networks of Scientific Papers." Science 149:510-15.
------. 1970. "Citation Measures of Hard Science, Soft Science, Technology and Non- Science." In C. E. Nelson, and D. K. Pollock (Eds.), Communication among Scientists and Engineers, pp. 1-15. Lexington, MA: D. C. Heath.
Reid, M. J., and J. M. Moran. 1988. "Astronomical Masers." In G. L. Verschuur and K. I. Kellerman (Eds.), Galactic and Extragalactic Radio Astronomy, pp. 255-94. New York: Springer-Verlag.
Reskin, B. F. 1977. "Scientific Productivity and the Reward Structure of Science." American Sociological Review 42:491-504.
Roose, K. D., and C. J. Andersen. 1970. A Rating o f Graduate Programs. Washington, DC: American Council on Education.
Science Citation Index. 1975-1979. Philadelphia: Institute for Scientific Information.
Shadish, W. R., D. Tolliver, M. Gray, and S. K. S. Gupta. 1995. "Author Judgements about Works They Cite: Three Studies from Psychology Journals." Social Studies o f Science 25:477-98.
156 Shaw, G. K. 1984. Rational Expectations: An Elementary Exposition. Brighton: Wheatsheaf Books.
Small, H. 0.1977. "A Co-citation Model of a Scientific Specialty: A Longitudinal Study of Collagen Research." Social Studies o f Science 7:139-66.
------1978. "Cited Documents as Concept Symbols." Social Studies o f Science 8:327- 40.
Small, H. G., and B. C. Griffith. 1974. "The Structure of Scientific Literatures I: Identifying and Graphing Specialties."Science Studies 4:17-40.
Snyder, L. E., and D. Buhl. 1974. "Detection of Possible Maser Emission Near 3.48 Millimeters firom an Unidentified Molecular Species in Orion."The Astrophysical Journal (Letters) 189:L31-L33.
Social Science Citation Index. 1977-1981. Philadelphia: Institute for Scientific Information.
Spiegel-Rosing, I. 1977. "Science Studies: Bibliometric and Content Analysis." Social Studies o f Science 7:97-113.
Stewart, J. A. 1983. "Achievement and Ascriptive Processes in the Recognition of Scientific Articles." Social Forces 62:166-89.
------1990. Drifting Continents and Colliding Paradigms: Perspectives on the Geoscience Revolution. Bloomington: Indiana University Press.
Stigler, G. J. n.d. Personal communication with L. L. Hargens.
Storer, N. W. 1966. The Social System o f Science. New York: Holt, Rinehart and Winston.
------. 1967. "The Hard Sciences and the Soft: Some Sociological Observations." Bulletin o f the Medical Library Association 55:75-84.
1972. "Relations among Scientific Disciplines." In S. Z. Nagi and R. G. Corwin (Eds.), The Social Contexts o f Research, pp. 229-68. New York: Wiley.
Studenmund, A. H. 1992.Using Econometrics: A Practical Guide, 2nd ed. New York: Harper Collins.
Sudman, S. 1976. Applied Sampling. New York: Academic Press.
157 Tagliacozzo, R. 1977. "Self-Citations in Scientific Literature." Journal o f Documentation 33:251-65.
Thome, F. C. 1977. "The Citation Index: Another Case of Spurious Validity." Journal o f Clinical Psychology 33:1157-61.
Ulrich's International Periodicals Directory. 1980. New York: Bowker.
Ward, K. B., J. Gast, and L. Grant. 1992. "Visibility and Dissemination of Women's and Men's Sociological Scholarship." Social Problems 39:291-98.
Weaver, H., D. R. W. Williams, N. H. Dieter, and W. T. Lum. 1965. "Observations of a Strong Unidentified Microwave Line and of Emission from the OH Molecule." Nature 208:29-31.
Weinstock, M. 1971. "Citation Indexes." Encyclopedia of Library & Information Science 5:16-40.
White, H. D., and B. C. Griffith. 1982. "Authors as Markers of Intellectual Space." Journal o f Documentation 38:255-72.
Whitley, R. 1984. The Intellectual and Social Organization o f the Sciences. Oxford: Clarendon Press.
Winch, P. 1958. The Idea o f Science. London: Routledge & Kegan Paul.
Yuan, X. 1994.Citation Network and Social Ties. Columbus: The Ohio State University (M.A. Thesis).
Zuckerman, H. 1977. Scientific Elite: Nobel Laureates in the United States. New York: Free Press.
Zuckerman, H., and R. K. Merton. 1971. "Patterns of Evaluation in Science: Institutionalization, Structure and Functions of the Referee System."Minerva 9:66-100.
. 1972. "Age, Aging and Age Structure in Science." In M. W. Riley, M. E. Johnson, and A. Foner (Eds.), Aging and Society, pp. 292-356. New York: Russell Sage.
158 APPENDIX A: LIST OF THE 100 ARTICLES IN THE
CELESTIAL MASERS SAMPLE
1. Letokhov, V. S. 1966. "Stimulated Radio Emission of the Interstellar Medium." ZhETF Pis'ma 4:477-481.
2. Bender, P. L. 1976. "Polarization of Cosmic OH 18-cm Radiation." Physical Review Letters 18:562-564.
3. Ryle, M., and D. Downes. 1967. "High-Resolution Radio Observations of an Intense H II Region in Cygnus X." The AstrophysicalJourml 148:L17-L21.
4. Robinson, B. J., and R. X. McGee. "OH Molecules in the Interstellar Medium." Annual Review of Astronomy and Astrophysics 5:183-212.
5. Raimond, E., and B. Eliasson. 1967. "Possible Relation Between an OH Source and an Infared Object in the Orion Nebula." The Astrophysical Journal 150:L171-L173.
6 . Gold, T. 1967. "Maser Action in Space." In Y. Terzian (Ed.), Interstellar Ionized Hydrogen, pp. 747-759. New York: W. A. Benjamin.
7. Weaver, H., N. H. Dieter, and D. R. W. Williams. 1967. "Observations of OH Emission in W3, NGC 6334, W49, W51, W75, and ORI A." The Astrophysical Journal Supplement Series 146:219-274.
8 . Ball, J. A., and D. H. Staelin. 1968. "Classification of OH Radio Emission Sources." The Astrophysical Journal 153 :L41 -L47.
9. Zuckerman, P. P., H. Penfield, and A. E. Lilley. 1968. "Detection of Microwave Radiation From the 211,/j, J=l/2 State of OH." The Astrophysical Journal 153:L69-L76.
10. Holtz, J. Z. 1968. "Energy Requirements and Mechanisms for OH Galactic Masers." The Astrophysical Journal 153:L117-L121.
159 11. Robinson, B. J., W. M. Goss, and R. N. Manchester. 1969. "A Thermal Source With Strong 1612 MHz OH Emission." Proceedings o f the Astronomical Society o f Australia 5:211-212.
12. Snyder, L. E., D. Buhl, B. Zuckerman, and P. Palmer. 1969. "Microwave Detection of Interstellar Formaldehyde." Physical Review Letters 22:679-681.
13. Burke, B. F. 1969. "Long-Baseline Interferometry." Physics Today 22:54-63.
14. Shklovskii, I. S. 1969. "The Nature of Sources of Radiation in OH Lines." Soviet Astronomy 13:1-4.
15. Litvak, M. M. 1969. "Hydroxyl and Water Masers in Protostars." Science 165:855- 861.
16. Schwartz, P. R., and A. H. Barret. 1970. "Microwave Water-Vapor Emission from Variable Stars." Bulletin o f the American Astronomical Society 2:343.
17. Manchester, R. N., B. J. Robinson, and W. M. Goss. 1970. "18 CM Observations of Galactic OH from Longitudes 128 to 300 ." Australian Journal o f Physics 23:751-775.
18. Turner, B. E., D. Buhl, E. B. Churchwell, P. G. Mezger, and L. E. Snyder. 1970. "Observations of Interstellar Water Vapor." Astronomy and Astrophysics 4:165- 172.
19. Wilson, W. J., and A. H. Barret. 1970. "OH Radio Emission Associated with Infared Stars." The Astrophysical Journal 160:545-571.
20. Thacker, D. L., W. J. Wilson and A. H. Barrett. 1970. "Observations of the ^11,/ 2 , J=l/2 State of OH." The Astrophysical Journal 16LL191-L197.
21. Gardner, F. F., J. C. Ribes, and W. M. Goss. 1970. "Emission of the Excited State of OH at 6035 MHz from NGC 6334." Astrophysical Letters 7:51-53.
22. Turner, B. E. 1971. "OH as a Constituent of the Interstellar Medium." In C. de Jager (Ed.), Highlights o f Astronomy 2, pp. 378-390. Dordrecht: D. Reidel.
23. Johnston, K. J., S. H. Knowles, W. T. Sullivan III, J. M. Morgan, F. B. Burke, K. Y. Lo, D. C. Papa, G. D. Papadopoulos, P. R. Schwartz, C. A. Knight, 1.1. Shapiro, andW. J. Welch. 1971. "An Interferometer Map of the Water-Vapor Sources in W49." The Astrophysical Journal 166:L21-L26.
160 24. Turner, B. E., and R. H. Rubin. 1971. "New Galactic HjO Sources Associated with H II Regions." The Astrophysical Journal 170:L113-Ll 18.
25. Peters, G. I., and L. Allen. 1972. "Spectral Linewidth in Laser Amplifiers and Amplified Spontaneous Emission, and its Relevance to the Interstellar Medium." Physics Letters 39A:259-260.
26. Wilson, A. J., R. D. Davies, and J. Ellder. 1972. "The Variability of the OH Source Wj." Monthly Notices o f the Royal Astronomical Society 157:21P-26P.
27. Boyd, R. W., and M. W. Werner. 1972. "Interstellar Scattering and the Sizes of Astronomical Masers." The Astrophysical Journal 174:L137-140.
28. Hills, R., M. A. Janssen, D. D. Thornton, and W. J. Welch. 1972. "Interferometric Positions of the Water-Vapor Emission Sources in H ,i Regions."The Astrophysical Journal 175;L59-164.
29. Ball, J. A., K. J. Johnson, S. H. Knowles, and J. M. Moran. 1972. "Interferometer Observations of the ^113/2, J=5/2 Microwave Transition of OH." Bulletin o f the American Astronomical Society 4:308-309.
30. Wynn-Williams, C. G., E. E. Becklin, and G. Neugebauer. 1972. "Infi-a-Red Sources in the H „ Region Wj." Monthly Notices o f the Royal Astronomical Society 160:1- 14.
31. Evans, N. J., R. E. Hills, O. E. H. Rydbeck, and E. Kollberg. 1972. "Statistics of the Radiation firom Astronomical Masers." Physical Review 6:1643-1647.
32. Turner, B. E., M. A. Gordon, and G. T. Wrixon. 1972. "Detection of the 4,-3o (E^) Line of Interstellar Methyl Alcohol." The Astrophysical Journal 177:609-617.
33. Burke, B. F., K. J. Johnston, V. A. Efanov, B. G. Clark, L. R. Kogan, V. I. Kostenko, K. Y. Lo, L. I. Matveenko, I. G. Moiseev, J. M. Moran, S. H. Knowles, D. C. Papa, G. D. Papadopoulos, A. E. E. Rogers, and P. R. Schwartz. 1972. "Observations of Maser Radio Sources with an Angular Resolution of 0".0002." Soviet Astronomy 16:379-382.
34. Litvak, M. M. 1973. "Masers and Optical Pumping." In M. A. Gordon (Ed.), Molecules in the Galactic Environment, pp. 267-288. New York: Wiley.
35. Burdyuzha V. V., and D. A. Varshalvich. 1973. "Spin Alignment of OH Molecules by Infared Radiation." Soviet Astronomy 16:597-603.
161 36. Strel'nitskii, V. S. and R. A. Syunyaev. 1973. "Nature of the High Velocities of H^O Sources in W49." Soviet Astronomy 16:579-584.
37. Knowles, S. H., K. J. Johnston, J. M. Moran, and J. A. Ball. 1973. "Interferometric
Observations of the ^11 3 / 2 J=5/2 State of Interstellar OH." The Astrophysical Journal 180:L117-121.
38. Burdyuzha, V. V., and D. A. Varshalovich. 1973. "Infared and Radio Transition Probabilities of OH and CH." Soviet Astronomy 16:980-982.
39. Goldriech, P., D. A. Keeley, J. Y. Kwan. 1973. "Astrophysical Masers. 111. Trapped Infiared Lines and Cross-Relaxation." The Astrophysical Journal 182:55-66.
40. Moran, J. M., G. D. Papadopoulos, B. F. Burke, K. Y. Lo, P. R. Schwartz, D. L. Thacker, K. J. Johnston, S. H. Knowles, A. C. Reisz, and 1.1. Shapiro. 1973. "Very Long Baseline Interferometric Observations of the H20 Sources in W49 N, W3(0H), Orion, A, and, VY Canis Majoris." The Astrophysical Journal 185:535- 567.
41. Strel'niskii V. S. 1972. "Pumping Mechanisms for Maser HjO Sources." Soviet Astronomy 17:717-724.
42. Rydbeck, O. E. H., J. Ellder, and W. M. Irvine. 1973. "Radio Detection of Interstellar CH." Nature 246:466-468.
43. Turner, B. E., and B. Zuckerman. 1974. "Microwave Detection of Interstellar CH." The Astrophysical Journal 187:L59-L62.
44. Schwartz, P. R., P. M. Harvey, and A. H. Barrett. 1974. "Time Variation of the H20 Maser and Infrared Continuum in Late-Type Stars."The Astrophysical Journal 187:491-496.
45. Snyder, L. E., and D. Buhl. 1974. "Detection of Possible Maser Emission Near 3.48 Millimeters from an Unindentified Molecular Species in Orion."The Astrophysical Journal 189:L31-L33.
46. Herbig, G. H. 1974. "Structure of the OH/lnfared Object NML Cygnus. II. Analysis of the OH Interferometry." The Astrophysical Journal 189:75-79.
47. Baudry, A. 1974. "Observations of the Excited Lines of OH at a Wavelength of 6.3 cm." Astronomy and Astrophysics 33:381-384.
162 48. Davis, J. H., G. N. Blair, H. Van Till, and P. Thaddeus. 1974. "Vibrationally Excited Sillicon Monoxide in the Orion Nebula." The Astrophysical Journal 190:L 117- L119.
49. Thaddeus, P., J. Mather, J. H. Davis and G. N. Blair. 1974. "Detection of the J = l-O Rotational Transition of Vibrationally Excited Silicon Monoxide." The AstrophysicalJournal 192:L33-L36.
50. Harvey, P. J., R. S. Booth, R. D. Davies, D. C. B. Whittet, and W. McLaughlin. 1974. "Interferometric Observations of the Structure of Main-Line OH Sources." Monthly Notices o f the Royal Astronomical Society 169:545-576.
51. Knowles, S. H., K. J. Johnston, J. M. Moran, B. F. Burke, K. Y. Lo, and G. D. Papadopoulos. 1974. "Further Interferometer Observations of the Water Vapor Sources in W49." The Astronomical Journal 79:925-932.
52. Keeley, D. 1974. "The Saturation Behavior of Nonuniformly Pumped Masers." The Astrophysical Journal 192:601-606.
53. Pelling, M. 1975. "A Line-Leaking Model for Silicon Monoxide Millimetre Wavelength Emission." Monthly Notices o f the Royal Astronomical Society 172:421-425.
54. Mader, G. L., K. J. Johnston, J. M. Moran, S. H. Knowles, S. A. Mango, P. R. Schwartz, and W. B.Waltman. 1975. "The Relative Positions of the OH and H^O Masers in W49N and W3(0H)." The Astrophysical Journal 200:L111-Ll 14.
55. Lo, K. Y., B. F. Burke, and A. D. Haschick. 1975. "HjO Sources in Regions of Star Formation." The Astrophysical Journal 202:81-91.
56. Lo, K. Y., R. C. Walker, B. F. Burk, J. M. Morgan, K. J. Johnston, and M. S. Ewing. 1975. "Evidence for Zeeman Splitting in 1720-MHz OH Line Emission." The Astrophysical Journal 202:650-654.
57. Knowles, S. H., J. L. Caswell, and W. M. Goss. 1976. "Excited OH Radition from the ^IIj^, J=5/2 State in Southern H II Regions." Monthly Notices o f the Royal Astronomical Society 175:537-555.
58. Gammon, R. H. 1975. "Correlated Variability in W 4 9 (H2 0 )." Astronomy and Astrophysics 50:71-77.
59. Morris, M., and G. R. Knapp. 1976. "Detection of H,0 Maser Emission from Four Infrared Sources." The Astrophysical Journal 204:415-419.
163 60. Morris, M., B. E. Turner, P. Palmer, and B. Zuckerman. 1976. "Cyanoacetylene in Dense Interstellar Clouds." The Astrophysical Journal 205:82-93.
61. Rydbeck, O. E. H., E. Kollberg, A. Hjalmarson, A. Sume, and J. Ellder. 1976. "Radio Observations of Interstellar Ch. I." The Astrophysical Journal Supplement Series 31:333-415.
62. Shmeld, I. K., V. S. Strel'nitskii, and V. V. Muzylev. 1976. "Collisional Pumping of a Cosmic H20 Maser in a Shock Wave." Soviet Astronomy 20:411-418
63. Broten, N. W., J. M. MacLeod, T. Oka, L. W. Avery, J. W. Brooks, R. X. McGee, and L. M. Newton. 1976. "Evidence for Weak Maser Action in Interstellar Cyanodiacetylene." The Astrophysical Journal 209:L143-147.
64. Cochran, W. D., and J. P. Ostriker. 1977. "The Development of Compact Dust- Bounded H „ Regions. I. Their Relation to Infrared Objects and Maser Sources." The Astropf^sical Journal 211:392-399.
65. Balister, M., R. A. Batcheor, R. F. Haynes, S. H. Knowles, M. G. McCulloch, B. J. Robinson, K. J. Wellington, and D. E. Yabsley. 1977. "Observations of SiO Masers at 43 GHz with the Parkes Radio Telescope." Montly Notices o f the Royal Astronomical Society 180:415-427.
6 6 . Dickinson, D. P., and S. G. Kleinmann. 1977. "Shell Structure in Stellar Water Masers." The Astrophysical Journal 214:L135-136.
67. Forster, J. R., W. J. Welch, and M. Ç. H. Wright. 1977. "Accurate HgO Source Positions in W3." The Astrophysical Journal 2\5:L121-L125.
6 8 . Crutchner, R. M. 1977. "Excitation of OH Toward Interstellar Dust Clouds." The Astrophysical Journal 216:308-319.
69. Buxton, R. B., A. H. Barret, P. T. P. Ho, and M. H. Scheps. 1977. "Search for Methanol Masers." The Astronomical Journal 82:985-988.
70. Hansen, S. S., J. M. Moran, M. J. Reid, K. J. Johnson, J. H. Spencer, and R. C. Walker. 1977. "The Hydroxyl Masers in the Orion Nebula." The Astrophysical Journal 218:L65-169.
71. Elitzur, M. 1978. "OH Main Lines Masers I: OH/IR Stars." Astronomy and Astrophysics 62:305-309.
164 72. Salem, M., and M. S. Middleton. 1978. "Time Variability of Astrophysical Masers." Monthly Notices o f the Royal Astronomical Society 183:491-500.
73. Lada, C. J., and M. J. Reid. 1978. "CO Observations of a Molecular Cloud Complex Associated with the Bright Rim Near VY Canis Majoris." The Astrophysical Journal 219:95-104.
74. Reid, M. J., and D. O. Muhleman. 1978. "Very Long Baseline Interferometric Observations of the Hydroxyl Masers in VY Canis Majoris." The Astrophysical Journal 220:229-238.
75. Scoville, N. Z., and P. M. Solomon. 1978. "Vibrationally Excited Carbon Monoxide in IRC +10216." The Astrophysical Journal 220:L103-L107.
76. Genzel, R., D. Downes, J. M. Moran, K. J. Johnston, J. H. Spencer, R. C. Walker, A. Haschick, L. I. Matveyenko, L. R. Kogan, V. I. Kostenko, B. Ronnang, O. E. H. Rydbeck, and 1. G. Moiseev. 1978. "Structure and Kinematics of HjO Sources in Clusters of Newly-Formed OB Stars." Astronomy and Astrophysics 66:13-29.
77. Elitzur, M., and T. de Jong. 1978. "A Model for the Maser Sources Associated with H II Regions." Astronomy and Astrophysics 67:323-332.
78. Fernandez, J. C., and G. Reinisch. 1978. "Generation of Very-High-Velocity Satellite- Features Through Stimulated Raman Scattering of 22.2 GHz H;0 Maser-Lines in Compact H ^ Plasma Regions. One Dimensional Model." Astronomy and Astrophysics 67:163-174.
79. Lepine, J. R. D., A. M. Le Squeren, and E. Scalise, Jr. 1978. "Observations of SiO Maser Sources at 34.122 GHz." The Astrophysical Journal 225:869-879.
80. Burke, B. F., T. S. Giufifrida, and A. D. Haschick. 1978. "Maser Time Variations." The Astrophysical Journal 226:L21-L24.
81. Bettwieser, E. 1979. "Remarks on Time Variations and Radiative Stability of the Celestial Masers." Astronomy and Astrophysics 72:97-103.
82. Genzel, R., and D. Downes. 1979. "HjO in the Galaxy. II. Duration of the Maser Phase and the Galactic Distribution of H^O Sources." Astronomy and Astrophysics 72:234-240.
83. Elmegreen, B. G., and J. M. Moran. 1979. "Observations of the Shock in a Region of Shock-Induced Star Formation: NGC 281." The Astrophysical Journal 227:L93- L96.
165 84. Evans, N. J., II, S. Beckwith, R. L. Brown, and W. Gilmore. 1979. "Type I OH Masers: A Study of Positions, Polarization, Nearby Water Masers, and Radio Continuum and Infrared Properties."The Astrophysical Journal 227:450-465.
85. Elitzur, M. 1979. "OH Main Line Masers." Astronomy and Astrophysics 73:322-328.
8 6 . Norman, C., and J. Silk. 1979. "Interstellar Bullets: H^O Masers and Herbig-Haro Objects." The Astrophysical Journal 228:197-205.
87. Mutel, R. L., J. D. Fix, J. M. Benson, and J. C. Webber. 1979. "High-Resolution OH Maser Observations of IRC +10420." The Astrophysical Journal 228:771-779.
8 8 . Dieter, N. H., W. J. Welch, and M. C. H. Wright. 1979. "H 2 O Masers and H „ Regions in W49 at 23 GHz." The Astrophysical Journal 230:768-770.
89. Cahn, J. H., and M. Elitzur. 1979. "A Correlation Between SiO and Stellar Luminosities in Long-Period Variables and the Nature of the SiO Maser Pump Mechanism." The Astrophysical Journal 231:124-127.
90. Kroll, N. M., and W. A. McMullin. 1979. "Stimulated Linear Acceleration Bremsstrahlung." The Astrophysical Journal 231:425-437.
91. Genzel, R., J. M. Moran, A. P. Lane, C. R. Predmore, P. T. P. Ho, S. S. Hansen, and M. J. Reid. 1979. "VLBl Observations of the SiO Maser in Orion." The Astrophysical Journal 231:L73-L76.
92. Brocka, B. 1979. "A Survey of Symbiotic Stars at 1612 MHz." Publications o f the Astronomical Society o f the Pacific 91:519-520.
93. McBreen, B., G. G. Fazio, M. Stier, and E. L. Wright. 1979. "Evidence for a Variable Far-Infrared Source in NGC 6334." The Astrophysical Journal 232:L183-L187.
94. Phillips, J. P., and J. E. Beckman. 1980. "The Nature of the Kleinmann-Low Nebula." Monthly Notices o f the Royal Astronomical Society 193:245-260.
95. Morris, M. 1980. "Molecular Emission from Expanding Envelopes Around Evolved Stars. 111. Thermal and Maser CO Emission." The Astrophysical Journal 236:823- 834.
96. Lucas, R. 1980. "The Pumping of Interstellar OH Main-Line Masers: An Efficient Mechanism." Astronomy and Astrophysics 84:36-39.
166 97. Reid, M. J., A. D. Haschick, B. F. Burke, J. M. Moran, K. J. Johnston, and G. W. Swenson, Jr. 1980. "The Structure of Interstellar Hydroxyl Masers: VLBI Synthesis Observations of W3(0H)." The Astrophysical Journal 239:89-111.
98. Genzel, R., D. Downes, P. R. Schwartz, J. H. Spencer, V. Pankonin, and J. W. M. Baars. 1980. "SiO Emission in Orion-KL: An Evolved Star in a Region of Star Formation or a Unique Object in the Galaxy?" The Astrophysical Journal 239:519-525.
99. Krotkov, R., D. Wang, and N. Z. Scoville. 1980. "Ultraviolet Pumping of Molecular Vibrational States: The CO Infiared Bands." The Astrophysical Journal 240:940- 949.
100. Bowers, P. F., M. J. Reid, K. J. Johnston, J. H. Spencer, and J. M. Moran. 1980. "The Structure of OH Masers Around Late-Type Stars."The Astrophysical Journal 242:1088-1101.
167 APPENDIX B: LIST OF THE 100 ARTICLES IN THE
RATIONAL EXPECTATIONS SAMPLE
1. Pashigian, B. P. 1970. "Rational Expectations and the Cobweb Theory." Journal o f Political Economy 78:338-352.
2. Black, S. W. 1972. "The Use of Rational Expectations in Models of Speculation." Review o f Economics and Statistics 54:161-165.
3. Sargent, T. J. 1972. "Rational Expectations and the Term Structure of Interest Rates." Journal o f Money, Credit and BanMng 4:74-97.
4. Modigliani, P., and R. J. Shiller. 1973. "Inflation, Rational Expectations and the Term Structure of Interest Rates."Economica 40:12-43.
5. Sargent, T. J., and N. Wallace. 1973. "Rational Expectations and the Dynamics of Hyperinflation." International Economic Review 14:328-350.
6 . Sargent, T. J., and N. Wallace. 1975. "'Rational' Expectations, the Optimal Monetary Instrument, and the Optimal Money Supply Rule."Journal o f Political Economy 83:241-254.
7. Nelson, C. R. 1975. "Rational Expectations and the Estimation of Econometric Models." International Economic Review 16:555-561.
8 . Lucas, R. E., Jr. 1975. "An Equilibrium Model of the Business Cycle." Journal of Political Economy 83:1113-1144.
9. McCallum, B. T. 1976. "Rational Expectations and the Natural Rate Hypothesis: Some Consistent Estimates." Econometrica 44:43-52.
10. Sargent, T. J. 1976. "A Classical Macroeconometric Model for the United States." Journal o f Political Economy 84:207-237.
11. Barro, R. J. 1977. "Unanticipated Money Growth and Unemployment in the United States." American Economic Review 67:101-115.
168 12. Barro, R. J. 1977. "Long-Term Contracting, Sticky Prices, and Monetary Policy." Journal o f Monetary Economics 3:305-316.
13. Fischer, S. 1977. "Long-Term Contracting, Sticky Prices, and Monetary Policy: A Comment." Journal of Monetary Economics 3:317-323.
14. Mullineanx, D. J. 1978. "On Testing for Rationality: Another Look at the Livingston Price Expectations Data." Journal o f Political Economy 86:329-336.
15. Lucas, R. E., Jr. 1978. "Unemployment Policy." American Economic Review 68:353- 337.
16. Fair, R. C. 1978. "A Criticism of One Class of Macroeconomic Models with Rational Expectations." Journal o f Money, Credit and Banking 10:411-417.
17. McCallum, B. T. 1978. "Price Level Adjustments and the Rational Expectations Approach to Macroeconomic Stabilization Policy." Journal o f Money, Credit and Banking 10:418-436.
18. Hammann, V. D. 1979. "Phillips-Kurve, Rationale Erwartungen und die Kontrakttheoretische Betrachtung des Arbeitsmarktes." Konjunkturpolitik 25:156- 179.
19. McCallum, B. T. 1979. "Monetarism, Rational Expectations, Oligopolistic Pricing, and the MPS Econometric Model." Journal o f Political Economy 87:57-73.
20. Aoki, M., and M. Canzoneri. 1979. "Reduced Forms of Rational Expectations Models." Quarterly Journal o f Economics 93:59-71.
21. Decanio S. J. 1979. "Rational Expectations and Learning from Experience." Quarterly Journal o f Economics 93:47-57.
22. Taylor, J. B. 1979. "Estimation and Control of a Macroeconomic Model with Rational Expectations." Econometrica 47:1267-1286.
23. Fellner, W. 1979. "The Credibility Effect and Rational Expecations: Implications of the Gramlich Study." Brookings Papers on Economic Activity 1:167-178.
24. Small, D. H. 1979. "Unanticipated Money Growth and Unemployment in the United States: Comment." American Economic Review 69:996-1003.
169 25. Grossman, H. I. 1980. "Rational Expectations, Business Cycles, and Government Behavior." In S. Fischer, Rational Expectations and Economic Policy, pp. 5-22. Chicago: University of Chicago Press.
26. Barro, R. J. 1980. "Unanticipated Money and Economic Activity." In S. Fischer, Rational Expectations and Economic Policy, pp. 23-73. Chicago: University of Chicago Press.
27. Lucas, R. E., Jr. 1980. "Rules, Discretion, and the Role of the Economic Advisor." In S. Fischer, Rational Expectations and Economic Policy, pp. 199-210. Chicago: University of Chicago Press.
28. Fischer, S. 1980. "On Activist Monetary Policy with Rational Expectations." In S. Fischer, Rational Expectations and Economic Policy, pp. 211-247. Chicago: University of Chicago Press.
29. Berkman, N. G. 1980. "A Rational View of Rational Expectations." New England Economic Review January/February: 18-29.
30. Frankel, J. A. 1980. "Tests of Rational Expectations in the Forward Exchange Market." Southern Economic Journal 46:1083-1101.
31. Wogin, G. 1980. "Unemployment and Monetary Policy Under Rational Expectations: Some Canadian Evidence." Journal o f Monetary Economics 6:59-68.
32. Fields, T. W., and N. R. Noble. 1980. "Rational Expectations and the Short-Run Phillips Curve: Conunent."Journal of Macroeconomics 2:185-186.
33. Hall, R. E. 1980. "The Rational Expectations Approach to the Consumption Function: A Multi-Country Study by Bilson: Comment." European Economic Review 13:301-303.
34. Froyen, R. T., R. N. Waud. 1980. "Further International Evidence on Output-Inflation Tradeoffs." American Economic Review 70:409-421.
35. Revankar, N. S. 1980. "Testing of the Rational Expectations Hypothesis." Econometrica 48:1347-1363.
36. Barro, R. J. 1980. "A Capital Market in an Equilibrium Business Cycle Model." Econometrica 48:1393-1417.
37. Barro, R. J. 1980. "Federal Deficit Policy and the Effects of Public Debt Shocks." Journal o f Money, Credit and Banking 12:747-762.
170 38. Okun, A. M. 1980. "Rational Expectations with Misperceptions as a Theory of the Business Cycle." Journal o f Money, Credit, and Banking 12:817-825.
39. Buiter, W. H. 1980. "Monetary, Financial, and Fiscal Policies Under Rational Expectations." International Monetary Fund Staff Papers 27:785-813.
40. Colander, D. C., and R. S. Guthrie. 1980. "Great Expectations: What the Dickens Do 'Rational expectations' Mean?" Journal of Post Keynesian Economics 3:219-234.
41. Futia, C. A. 1981. "Rational Expectations in Stationary Linear Models." Econometrica 49:171-192.
42. Kreicher, L. L. 1981. "An International Evaluation of the Fischer Hypothesis Using Rational Expectations." Southern Economic Journal 48:58-67.
43. Maccini, L. J. 1981. "Adjustment Lags, Economically Rational Expectations and Price Behavior." The Review o f Economics and Statistics 63:213-222.
44. Shiller, J. 1981. "Alternative Tests of Rational Expectations Models: The Case of the Term Structure."Journal o f Econometrics 16:71-87.
45. Blanchard, J. 1981. "Output, the Stock Market, and Interest Rates." American Economic Review 71:132-143.
46. Driskill, R. 1981. "Exchange Rate Overshooting, the Trade Balance, and Rational Expectations." Journal o f International Economics 11:361-377.
47. Shiller, R. J. 1981. "The Use of Volatility Measures in Assessing Market Efficiency." Journal o f Finance 36:291-304.
48. Chow, G. C. 1981. "Estimation and Control of Rational Expectations Models." American Economic Review 71:211-216.
49. Blinder, A. S., and S. Fischer. 1981. "Inventories, Rational Expectations, and the Business Cycle." Journal o f Monetary Economics 8:277-304.
50. Gordon, R. J. 1981. "Output Fluctuations and Gradual Price Adjustment."Journal of Economic Literature 19:493-530.
51. Grossman, S. J. 1981. "An Introduction to the Theory of Rational Expectations Under Asymmetric Information." Review of Economic Studies 48:541-559.
171 52. Buiter, W. H. 1981. "The Superiority of Contingent Rules Over Fixed Rules in Models with Rational Expectations." The Economic Journal 91:647-670.
53. Sullivan, J. D. 1981. "Interrelations Between Spot and Futures Markets: Some Implications of Rational Expectations: Discussion."American Journal o f Agricultural Economics 63:942-943.
54. Dewbre, J. H. 1981. "Interrelationships Between Spot and Futures Markets: Some Implications of Rational Expectations."American Journal o f Agricultural Economics 63:926-933.
55. Helmberger, P. G., R. D. Weaver, and K. T. Haygood. 1982 "Rational Expectations and Competitive Pricing and Storage." American Journal o f Agricultural Economics 64:266-270.
56. Mitchell, D. W. 1982. "The Optimal Policy Rule Under Rational Expectations and Multiplier Uncertainty."Journal o f Economics and Business 34:129-133.
57. Minford, P. M., and D. Peel. 1982. "The Phillips Curve and Rational Expectations." Weltwirtschaftliches Archives 118:456-478.
58. Waldo, D. G. 1982. "Rational Expectations and the Role of Countercyclical Monetary Policy." Journal o f Monetary Economics 10:101-109.
59. Ulveling, E. F. 1982. "Stabilization Policy with Rational Expectations and . Information Costs." Rivista Internazionale di Scienze Economiche e Commerciali 29:596-610.
60. Cuddinton, J. T. 1982. "Canadian Evidence on the Permanent Income-Rational Expectations Hypothesis." Canadian Journal o f Economics 15:331-335.
61. Maddock, R., and M. Carter. 1982. "A Child's Guide to Rational Expectations." Journal o f Economic Literature 20:39-51.
62. Anderson, R. M. and H. Sonnenchein. 1982. "On the Existence of Rational Expectations Equilibrium."Journal o f Economic Theory 26:261-278.
63. Blume, L. E., M. M. Bray, and D. Easley. 1982. "Introduction to the Stability of Rational Expectations Equilibrium."Journal o f Economic Theory 26:313-317.
64. Bray, M. 1982. "Learning, Estimation, and the Stability of Rational Expectations." Journal o f Economic Theory 26:318-339.
172 65. Lowenberg, A. D. 1982. "A Critical Assessment of the Macro Rational Expectations Paradigm." South African Journal o f Economics 50:208-24.
6 6 . Tirole, J. 1982. "On the Possibility of Speculation Under Rational Expectations." Econometrica 50:1163-1181.
67. Goodwin, T. H., and S. M. Shefifin. 1982. "Testing the Rational Expectations Hypothesis in an Agricultural Market." Review o f Economics and Statistics 64:658-667.
6 8 . Lipton, D., J. Poterba, J. Sachs, and L. Summer. 1982. "Multiple Shooting in Rational Expectations Models." Econometrica 50:1329-1333.
69. Ormerod, P. 1982. "Rational and Non-rational Expectations of Inflation in Wage Equations for the United Kingdom." Economica 49:375-387.
70. Charemza, W., and M. Gronicki. 1982. "Rational Expectations and Disequilibria in a Model of Foreign Trade Behavior: The Case of Poland." Economics o f Planning 18:53-64.
71. Hsieh, D. A., and N. Kulatilaka. 1982. "Rational Expectations and Risk Premia in Forward Markets: Primary Metals at the London Metals Exchange." Journal o f Finance 37:1199-1207.
72. Burton D. 1983. "Devaluation, Long-term Contracts and Rational Expectations." European Economic Review 23:19-32.
73. Home, J. 1983. "Rational Expectations and the Defries-Williams Inflationary Expectatons Series: A Reply." Economic Record 59:293-94.
74. Bausor, R. 1983. "The Rational Expectations Hypothesis and the Epistemics of Time." Cambridge Journal o f Economics 7:1-10.
75. Hendry, D. F. 1983. "On Keynesian Model Building and the Rational Expectations Critique: A Question of Methodology." Cambridge Journal o f Economics 7:69- 75.
76. Craine, R. and G. A. Hardouvelis. 1983. "Are Rational Expectations for Real?" Greek Economic Review 5:5-32.
77. Benavie, A. 1983. "Optimal Monetary Policy Under Rational Expectations with a Micro-Based Supply Function."Journal o f Macroeconomics 5:149-166.
173 78. Haraf, W. S. 1983. "Tests of a Rational Expectations-Structurai Neutrality Model with Persistent Effects of Monetary Disturbances." Journal o f Monetary Economics 11:103-116.
79. McDonald, J. 1983. "A Solution to an Estimation Problem Involving Models with Rational Expectations." Journal o f Monetary Economics 11:381-386.
80. Kawai, M. 1983. "Price Volatility of Storable Commodities Under Rational Expectations in Spot and Futures Markets."International Economic Review 24:435-459.
81. Lapan, H. E., and W. Enders. 1983. "Rational Expectations, Endogenous Currency Substitution, and Exchange Rate Determination." Quarterly Journal o f Economics 98:427-439.
82. Shah, A. 1983. "Rational Expectations Macro Models with Possible Steady-State Inflation and Unemployment." Journal o f Macroeconomics 5:461-471.
83. Wegge, L. L., and M. Feldman. 1983. "Identifiability Criteria for Muth-Rational Expectations Models." Journal o f Econometrics 21:245-254.
84. Wegge, L. L., and M. Feldman. 1983. "Comment to the Editor." Journal o f Econometrics 21:255-256.
85. Macdonald, R. 1983. "Some Test of the Rational Expectations Hypothesis in the Foreign Exchange Market." Scottish Journal o f Political Economy 30:235-250.
86. Carraro, C. 1983. "The Optimality of Economic Policy in the Rational Expectations Models." Giornale degli Economisti e Annali di Economic 42:643-665.
87. Johnson, P. 1983. "Life Cycle Consumption Under Rational Expectations: Some Australian Evidence." Economic Record 59:345-350.
88. Jones, D. S., and V. V. Roley. 1983. "Rational Expectations and the Expectations Model of the Term Structure: A Test Using Weekly Data." Journal o f Monetary Economics 12:453-465.
89. Snower, D. J. 1984. "Rational Expectations, Nonlinearities, and the Effectiveness of Monetary Policy." Oxford Economic Papers 84:177-199.
90. Cheng, H. C. 1984. "On the Generic Existence of Fully Revealing Price Equilibria." Journal o f Economic Theory 32:351-358.
174 91. Gray, J. A. 1984. "Dynamic Instability in Rational Expectations Models: An Attempt to Clarify." International Economic Review 25:93-122.
92. Visco, I. 1984. "On Linear Models with Rational Expectations: An Addendum." European Economic Review 24:113-115.
93. Kinkley, C. C., and K. Lahiri. 1984. "Testing the Rational Expectations Hypothesis in a Secondary Materials Market." Journal o f Environmental Economics and Management 11:282-291.
94. Rotemberg, J. J. 1984. "Interpreting the Statistical Failure of Some Rational Expectations Macroeconomic Models." American Economic Review 74:188-193.
95. Blume, L. E., and D. Easely. 1984. "Rational Expectations Equilibrium: An Alternative Approach."Journal o f Economic Theory 34:116-129.
96. Andersen, T. M. 1984. "Interest-Rate Policy and Rational Expectations." Journal of Macroeconomics 6:311-322.
97. Bigman, D. 1984. "Semi-Rational Expectations and Exchange-Rate Dynamics." Journal o f International Money and Finance 3:51-66.
98. Allen, P., and A. Postlewaite. 1984. "Rational Expectations and the Measurement of a Stock's Elasticity of Demand." Journal o f Finance 39:1119-1125.
99. Canto, V. A., D. H. Joines, and R. I. Webb. 1984. "Taxation, Rational Expectations, and the Neutrality of Money." Journal o f Macroeconomics 6:69-78.
100. Jaeger, V. K. 1984. "Persistence and Cyclical Movements of Unemployment in Equilibrium Models with Rational Expectations." Zeitschrift fur Wirtschafts und Sozialwissenschaften 104:645-673.
175