“No room for thinking, under the dome”. Flat earth and the boundary construction between science and non-science on Twitter.
Luca Carbone, ANR: 416453, SNR: 2013251
Abstract: With few exceptions, most of human history since the sixth century B.C. has been guided by the belief that the earth is round. Nevertheless, in recent times, the view proposing a flat earth has increased in popularity. Drawing from the literature about boundaries and boundary-work from Gyerin and Abott, this study aims at exploring how the boundaries between those defending science and non-science are constructed in public space. Given that the Flat Earth Society (FES) is almost uniquely present online, the normative power of defining knowledge cannot be evaluated in its core dimensions – through argumentations in scientific journals. For this reason, the periphery of science, its connections with the public and the narratives adopted on Twitter, is the principal avenue where I study the acquisition of normative and classification power to define knowledge systems. Based on a qualitative content analysis, this paper shows that FES supporters and adversaries are heterogeneous in the argumentations held to sustain their positions, something that complicates the general view of these groups as homogeneous groups. Based on a network analysis I analyze the boundaries between supporters and adversaries of the theory that the earth is flat. The boundary between these two factions appears to be mostly defined by FES adversaries through framing strategies such as debasement and homogenization of FES supporters. The results show how communication defines social and normative boundaries between science and non-science. At the end of the paper, I discuss the relevance of these findings for theories about the constructivist nature of science. Introduction With few exceptions, most of human history since the sixth century B.C. has been guided by the belief that the earth is round (Russell, 1997). Nevertheless, in recent times, the view proposing a flat earth has gained momentum, generating hypotheses, theories, experiments, and, in the end, a global movement amounting to 72.5 thousand followers on Twitter, now called Flat Earth Society (FES). Despite the scientific consensus around the shape of the Earth, the debate on this issue is thriving in the wider society1. This raises many questions about the reasons, modalities, and mechanisms through which discrepancies between science and non-science take shape. Two quotations from FES’ supporters might give an idea of the heterogeneous positions that could be found in this type of relationship.
“Can anybody confirm what 'gravity' is please? ��� didn't think so. Keep spending tax payers money working on that explanation though ��� #wasteofmoney #flatearth #wakeup #thetruthisoutthere”
“#Atheist #Globies �: #HappySabbath �: #Research #Biblical #FlatEarth Psalms 96:10 (#KJV) Say among the heathen that the LORD reigneth: the world also shall be established that it shall not be moved: he shall judge the people righteously. #CatholicTwitter : #NASA �”
The two positions have different points of departure to sustain the same view: while the former expresses distrust toward the scientific establishment and the impossibility to make a claim without first-hand experiences, the skepticism of the second comes from a religious branch of the movement, where the claim that the earth is flat derives from the Bible. These tweets highlight two fundamental problems in the scientific literature about the discrepancy between science and non-science. The first regards the view that non-science is internally homogeneous (Harambam & Aupers, 2015, 2016), while the second remarks the neglection of questions about who holds the classificatory power to demarcate systems of knowledge (Agrawal, 1995, 2002). Regarding the first issue, Harambam and Aupers (2015, 2016) point to the limits that academic works on conspiracy theories (CT) have in conceiving them as a dysfunctional product of society, the fruit of paranoia, bad science or religious beliefs. This leads to a preemptive portrayal of CT as devoid of any agency or project, as a monolithic and homogeneous entity. Challenging this approach, the authors show not only the presence of different reasons, worldviews, and practices among the same CT, but also different ways in which their proponents define themselves, as critical
1 https://www.economist.com/graphic-detail/2017/11/28/americas-flat-earth-movement-appears-to-be-growing, https://www.sciencefocus.com/the-human-body/the-rise-of-the-flat-earthers/, https://www.newyorker.com/science/elements/looking-for-life-on-a-flat-earth 2 thinkers instead of conspiracy theorists, for example. The homogeneous depiction of non-scientific efforts by science is also detectable for FES. A quick search in Google Scholar or Web of Science shows the lack of scientific articles addressing Flat Earth Society as phenomenon of interest. When addressed, it represents a negative metaphor to ask questions like “Are we at risk of becoming members of the Flat Earth Society by continuing to use scientifically inaccurate terminology?” (Nelson, 2014, p. 190) FES becomes the archetype for everything that is inaccurate and non-scientific. With regard to the second issue, the general category of non-science constitutes the negative counterpoint of science, something that establishes a normative classificatory system. This dichotomization hides a more variegated group: not only the macro category of non-scientific disciplines encompasses a variety of subcategories, such as conspiracy theories, alternative forms of knowledge, and para-disciplines. Each subcategory is a particular milieu in itself, including and binding together different ideas and positions. Moreover, within the group of non-scientific fields, there are categories whose definition is externally attributed, such as those with the attached labels of para-scientific and alternative (to science). This makes science a benchmark against which every other form of knowledge is measured, something that can be attributed to its cultural authority (Gauchat, 2011). Does FES undergo the same normative classificatory process? In order to address this question, it should first be asked under which type of non-science it is possible to classify FES. According to their online forum, “The Flat Earth Society has dedicated itself to starting science afresh from the ground up, to begin to see the world without bias and assumption. Experiment and experience has shown that the earth is decidedly flat. Time and time again through test, trial, and experiment, it has been shown that the earth is not a whirling globe of popular credulity, but an extended plane of times immaterial.”2 Moreover, FES sustains the idea of a conspiracy promoted by “NASA, its constituents and fellow so-called ‘space agencies’; as well as those who are informed by them (including government)”3 which are blamed for actively faking space travels. The reasons for this behavior are unknown; however “financial profit is the most commonly assumed motive.”4 Another idea is that because NASA has never been able to really go “any farther than the edge of the atmosphere, [t]he earth is portrayed as round in NASA media because the general public already believes that it is round.”3 Finally, “The US Government and its European allies have a large interest in investing untold millions of dollars into hoaxing space travel because it gives a superior image to the rest of the world.”3
2 https://theflatearthsociety.org/tiki/tiki-index.php?page=HomePage 3 https://theflatearthsociety.org/tiki/tiki-index.php?page=The%20Conspiracy 4 https://theflatearthsociety.org/tiki/tiki-index.php?page=Motive%20of%20the%20Conspiracy 3 From this brief overview of how the official FES website presents itself to the online public, it is possible to categorize this movement as a conspiracy theory against scientific institutions, justified by their involvement with political and financial powers. This is in line with the definition provided by Sustein & Vermeule (2009, p. 205), for whom conspiracy theories are “an effort to explain some event or practice by reference to the machinations of powerful people, who attempt to conceal their role (at least until their aims are accomplished).” Nevertheless, flat-earthers do not reject the scientific procedures of experiments and empirical evidence as tools to explain reality; rather, they bring them to their extreme consequences, for which only those able to see in the first person, know. In this sense, the Mertonian value of communism, for which scientists are asked to share their findings to the whole community, does not hold in their mentality, since it is outclassed by an extreme individualism. On the other hand, though, universalism (scientists evaluate findings according to preestablished impersonal criteria), disinterestedness (scientists do not have other motivations than the pure will of knowing), and organized skepticism (scientists do not dogmatically accept claims) are maintained and erected as lynchpins of their view. FES constitutes a peculiar example of how the distinction between science and non-science might be more subtle than generally portrayed in the scientific literature. Even if science is generally recognized for its adherence to certain values, for example, they are not necessarily its prerogative, and other entities can hold similar positions. Focusing on a specific movement, FES, instead of on a more general concept such as that of conspiracy theories, this study aims to provide more details about a specific group, delving deeper into their reasons, goals, and relationships with science. Moreover, it restricts the field of interest to online interactions, such as those happening on Twitter. In this way, it is possible to pursue the goal of self-definition, with users free to define themselves on both sides of the controversy, and that of boundary-work, the study of how certain opportunities – an online social network with 280 characters per message – are employed to bargain legitimacy on a conflictual turf, in order to determine how the boundaries between different entities are traced. To do so, the relationship between science and non- science is inserted within a constructivist perspective, with the idea that science is not only a content- based project (essentialist perspective), but also a system embedded within social projects. There are several questions that this work addresses: what are the main groups discussing FES on public avenues, such as Twitter? What arguments and strategies guide the bargaining process for the boundary between science and non-science? What is the most successful coalition in this process and why does it prevail? Is a normative classificatory system established?
Theoretical overview: FES in a field of scientific contestation Historical overview, principles, and relationship with scientific values
4 The theory of a flat earth had a scattered presence in ancient cultures (Garwood, 2007; Needham, 1986). Even if present in various societies and at different historical epochs, these ideas have never constituted the predominant perspective, maintained by the round earth position, which, since its first moves with Pythagoras, Parmenides, and Aristotle, has been continuously backed by empirical and experimental evidence (Dreyer, 1953 [1905]). Nevertheless, during the 19th century the English writer Samuel Rowbotham produced several pamphlets arguing that the "Bible, alongside our senses, supported the idea that the earth was flat and immovable and this essential truth should not be set aside for a system based solely on human conjecture." (Garwood 2007, p. 46) The religious nature of these claims were maintained also by Rowbotham’s successors until the 1956, when Samuel Shenton founded the International Flat Earth Research Society, reducing the emphasis on religious arguments and stressing the idea that photos shot by astronauts were distorted by the use of a wide-angle lens. After a small decline, the movement was resurrected in 2004 by Daniel Shenton, who founded a web- based discussion forum which is still alive and has acquired increasing consensus. As shown in the two tweets above, the movement has, since then, gathered a variegated group of supporters. In order to understand its composition and the normative classifications that are established between science and FES, a context where these positions actually interact is needed. The concepts of boundaries, entities, and boundary-work provide a vocabulary to describe this type of context.
Boundary-work: boundaries, entities, and social ground Regarding the relationship between an essentialist and a constructivist perspective, it is possible to argue that the cognitive authority granted to science mostly derives from the legitimacy acquired in public debates, rather than from its core qualities (methodologies, institutions, histories, consequences). The term coined by Gyerin (1995, p. 405) to define this process of authority acquisition is boundary-work, which “occurs as people contend for, legitimate, or challenge the cognitive authority of science – and the credibility, prestige, power, and material resources that attend such a privileged position.” Boundaries can be studied by looking at cultural manifestations, rather than balancing the argumentations about content, because “‘[u]nique’ features of science, qualities that distinguish it from other knowledge-producing activities, are to be found not in scientific practices and texts but in their representations.” (Gyerin 1995, p. 406) Hence, it is necessary to look at representations and narratives not only about science, (scientific divulgation and communication, Cassidy, 2006; Evans, 2009), but also about the relationships between science and non-science (Mizrachi & Shuval, 2005; Shuval & Mizrachi, 2004). This means focusing on how the defense of scientific borders is distributed
5 among different actors. For example, the actual content of conspiracy theories is never addressed in scientific publications, because conspiracy theorists never publish in scientific journals. Subsequently, scientists never address the problems raised in avenues that do not belong to the scientific community, which are left to be “debunked” by the rest of the society. Studying the process through which science defines legitimacy boundaries needs to look at the representations that the larger public provides of scientific issues. In order to have a better understanding of this argumentation, the concepts of boundary, entity, and social space have to be explained. First of all, boundaries should best be understood as the persistence and reproduction of forms of difference in a social space, a process generating entities. In this case, for example, differences in the normative power and cultural authority would be considered as boundaries when employed by actors to self-identify as scientific in order to classify others as non-scientific (and when this classification leads to positive consequences for the former and negative for the latter). In other words, “social entities come into existence when social actors tie social boundaries together in certain ways. Boundaries come first, then entities.” (Abbott, 1995a, p. 860; see also Abbott, 1995b) This means that science and non-science do not exist prior to their conflicts in the establishment of boundaries; rather, certain differences (values, methods, instruments, funds) are persistently yoked together in order to create a boundary which defines entities. Definitory power is the ability to delimit the boundaries that classify entities. This processual perspective permits the study of the emergence of science and non-science without considering them preexisting before any conflictual bargaining. Secondly, an entity is a combination of qualities that become internally coherent (i.e. the realization of one quality does not hinder the realization of the others) once aggregated under a recognizable label. Science, for example, is described by its epistemology (Popper, 1959), values (Merton, 1973), and fluctuating progress (Kuhn, 1962). Conspiracy theories maintain some of these characteristics (as shown above for FES), with the addition of solipsism and skepticism. These entities do not emerge prior to any form of relationship with other entities: they exist only in ecologies. Abbott’s (1988) approach to the study of professions paved the way for an ecological approach to knowledge systems, in competition with each other for the acquisition of legitimacy over certain issues. Boundaries are created through the appropriation of legitimacy claims, and the resulting entities define a certain set of qualities according to where the boundary lies. When these qualities become internally coherent and cohesive systems of beliefs, it is possible to define them as entities (Gyerin, 1983). Science and non-science can be considered as entities only when inserted within a conflictual field for the establishment of legitimacy boundaries. The third concept is that of social ground, otherwise defined as social space or field (Bourdieu, 1985; Abbott 1995a,b). According to classical electromagnetism theory, one of the most important
6 characteristics of fields is to “explain changes in the states of some elements.” (Martin, 2003, p. 4) In Bourdieusian terminology, the field consists of opportunity structures (rules of the field), in which actors (or entities) with certain positions (habitus and capital) take advantage of the opportunities offered by the field itself. Hence, the interaction between field structure and actors’ positions is what permits change. Conceived in this way, the process of boundary formation between science and non- science cannot prescind from considerations regarding the field in which it takes place. As presented above, when talking about the social dimensions of science, boundaries among these two entities cannot emerge in academic publications (because, by definition, non-scientific positions hardly access core areas), nor in institutional settings such as universities. These boundaries emerge in social settings where communication is open to everyone, such as on social networks. Twitter, in particular, has three main characteristics defining its rules as a field: the limitation of 280 characters per tweet, forcing users to express ideas with symbolically dense utterances, hashtags, and visual contents (e.g. videos, images, GIFs); the publicity of users’ profile information and timelines, for which everyone can see everything posted by other users; the openness for everyone to create a free profile and interact with other users. Besides these, there is a multitude of rules, more or less explicit, that guide the interaction between users, with the possibility of posting tweets and comments as expressions of personal thoughts, and retweets and likes to share and/or support others’ thoughts. In other words, Twitter is a particularly suitable field to study how entities bargain their boundaries through symbolically dense utterances, narratives and rhetorical styles (Geertz, 1973), in order to acquire legitimacy over certain areas. In conclusion, it could be said that boundaries are the final products of a process, boundary-work, which has the main function of dividing and defining entities. This conceptualization ensures that the presence of boundaries is not taken for granted, nor that of entities; rather, the stress is put on the process through which they are formed.
Boundary-work in action: structure of the discourse A constructivist approach conceives entities as the product of a conflictual relationship meant to establish normative and legitimacy boundaries, and fought on many fields with their own specific rules. Those governing Twitter are discursive. That is, given its symbolically dense and interactive environment, users’ behavior is guided by discursive choices (e.g. what type of words to use, what is the narrative behind hashtags). As mentioned in Leifeld & Haunss (2012, p. 383) “[t]he structure of the discourse constrains the set of feasible actions by political actors” and, because these actions constitute the bricks for the construction of a boundary, it is essential to analyze the properties of and the relationships between the entities emerging from discursive choices.
7 Binding this vocabulary with the one previously presented, the first step where entities with certain qualities take shape according to the rules of the field involves the articulation of their core content: in this situation, boundaries are created in the minds but not yet in practice. When conceived at a prenatal status, as differences in certain defining characteristics, they are not articulated to be put in practice; they are prescriptive. That is, they diagnose and identify a common problem (in this case, the shape of the earth), providing different solutions for it. Being put in practice means performing discursive strategies meant to acquire legitimacy (i.e. boundary-work), a process delimiting internally coherent systems, namely entities. Abbott’s dictum “[b]oundaries come first, then entities” (1995a, p. 860) should be better contextualized in its processual nature. Because boundaries do not come in a vacuum but in a field, and the field is created by preexisting entities with certain qualities, those stemming from the process of boundary-work cannot be compared with those before the process, even if the former derive from the latter. What distinguishes these two types of entities is their communicative enactment. In this process, a second step considers the performance of core contents among the public. In order to reach this step, where boundaries are formed and entities take shape, it should be explained how the conflict unfolds, that is how prescriptions are communicated and how they interact with other prescriptive guidelines. In other words, conflict and framing strategies become the object of study: who dominates the discussion and how? According to Leifeld and Haunss (2012, p. 385), “the dominant coalition will appear more prominently in the news media, gain a larger constituency, and it will be able to integrate the core frames into a more consistent storyline than its opponents. […] These frame alignment processes […] can only succeed if the members of a discourse coalition maintain a high level of congruence […]. In social network terms, the dominant discourse coalition should exhibit more clustering and a higher density on the ideational congruence relation.” In other words, the expectations for a dominant coalition are to be internally coherent and externally adversary, to be capable of integrating other argumentations and of defining, in this way, legitimacy boundaries. While conceived, pre-natal entities are prescriptive and negotiable (given their abstract nature, not yet in practice); when performed, entities stiffen, becoming exclusionary and exclusive.
Methods and Measures Data collection This study uses Twitter data fetched according to specific keywords that contains the binomial “flat earth”, namely #flatearth, #flatEarth, #FlatEarth, #flatearthsociety, #FlatEarthSociety, #FlatEarthers, #flat-Earthers. The data is collected using the packages ‘rtweet’ and ‘twitteR’ from the software R (version 1.1.383). It is only possible to collect a maximum of 18,000 tweets per
8 keyword published in the week previous to the collection day. I fetched the tweets on January the 15th 2019, and the final dataset consists of 7138 unique tweets from 4396 different users (3551 for flatearth, 3552 for flatEarth, 3532 for FlatEarth, 191 for flatearthsociety, 191 for FlatEarthSociety, 3975 for Flat Earthers, 3931 for flat-Earthers).
Content analysis: method and coding schema In order to have fine-grained details about the substance of the tweets, content analysis permits a systematic interpretative approach based on a coding schema. A qualitative analysis of the tweets has two main goals. On the one hand, it aims at constructing a typology of arguments in the FES debate employing the main argumentations through which certain views are articulated and supported. This will permit the construction of a dataset to perform the subsequent social network analysis. On the other hand, content analysis aims to find the framing strategies of each position. Given the lack of previous coding strategies developed for the relationship between science and non-science, this study adopts a conventional or inductive approach (Hsieh & Shannon, 2005), that is the development of a coding schema based on the reading of a sample of tweets. The main advantage of this approach is that it avoids previous theories to influence the choice of the categories of interest. In this study, the development of a coding schema is based on a random sample of 350 tweets, consisting of 5% of the total population. After this step, a second sample of 700 tweets (~10% of the whole population of tweets), different from the previous one, will be employed to conduct the main analysis and to define coalitions and arguments. This amount is chosen taking into account a trade-off among two elements: feasibility of the analysis with one coder and representativeness among the whole population. The main analysis will be carried out looking not only at the text of each tweet but also at the context in which it has been posted (e.g. as a comment to other tweets), as well as at the user who has posted it, in order to discern intentions of ambiguous tweets. The dimensions emerging from the first sample of 350 tweets, and presented in table 1, are four: • Tweets refers to the posted messages. The two characteristics are tendency (supporting or contesting FES), and type (sent by private user, public page or bot). Public pages are defined as pages, not single users, with more than 1000 followers. Bots are fake users, programmed to do an action such as writing or retweeting a post at scheduled times. • Argumentations are the reasons used to sustain a position (core) and the strategies employed to promote them (periphery). In this macro-category each sub-category has as a possible yes/no answer (excluded objectivism/solipsism), with yes expressing explicit acceptance of
9 that category and no explicit denial. This permits the detection of apparently paradoxical behaviors (for example, the use of scientific demonstration expressing, at the same time, distrust toward scientific institutions) and a more fine-grained definition of groups. These arguments will be used in the network analysis and, together with tweet tendency, allow the detection of coalitions. • Tone refers to the communicative nuances that are meant to convey a message in a certain way. Most of them are negative, such as mocking, angry, and bothered, one is neutral, and only one is positive (i.e. support), indicating the presence of highly conflictual relationships. • Attitudes gathers the ways in which the topic is approached and constitutes a category of clues to interpret how a tweet is constructed. Given the communicative field where tweets are shared, attitudes and tone constitute the performative strategies through which arguments are constructed and concepts become bricks for the erection of boundaries.
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Table 1: Coding schema
Main categories Subcategories 1 Subcategories 2 Subcategories 3 Pro FES Against FES Neutral (when talks about Tendency FES without a position) Uncategorized (when does Tweets not talk about FES but uses # or @) Private user Public/verified user (e.g. Type BuzzFeed) Bot Scientific demonstration (e.g. scientific, photos/videos) Coalitions Religion (e.g. the Bible as source) Core (i.e. epistemological Objectivism vs. solipsism assumptions) (e.g. truth as right and evident vs. truth as found individually) Frames/argumentation Scientific values (e.g. observable, testable, repeatable) Scientific trust Scam (e.g. use of Periphery (i.e. strategies to photoshop) weaken or reinforce one Paradox (e.g. FES using side) scientific methods against science)
Neutral
Of the method (e.g. YouTube) Extreme and absurd examples (e.g. “if having convictions means being a Mocking good person, FES are the Tone best”) Issue a challenge (e.g. “let’s try to go to the edge of earth”) Angry (e.g. insults) Bothered (e.g. “impossible
to reason with them”) Performative framing Supportive (e.g. awakening) Mixing (e.g. types of non- science, other ideas as feminism) Ad hoc attacks and generalizations (e.g. others
as bad people, lack of education) Attitudes Need to take action (e.g.
block FES, online debunk) Connections (e.g. with
politics, with the media) Curiosity (e.g. “I am just curious to know what do you think about…”)
11 Discourse network analysis: method and dimensions The first goal of DNA is to test whether the group typology stemming from the content analysis is supported by the connections between tweets and arguments. Two are the main advantages in flanking a qualitative evaluation of the subgroups with a network framework. First of all, the layout of the network – display of nodes – depends on a force-directed graph algorithm called KamadaKawai (Kamada & Kawai, 1989). This algorithm distributes nodes according to the graph theoretic distance between them – minimum path length connecting each node – and allows to say that the formation of subgroups in the network depends on the number of close relationships between nodes: the closer the nodes in the graph, the shorter the path between them, given their relationships with the rest of the network. The second advantage resides in the possibility to model the meaning of edges. As it is described below in the mathematical definition of the networks, the relationships between nodes could be of agreement or disagreement. This means that the display of the network does not only depend on the type of node but also on the type of edge. Groups are not only evaluated on the basis of their argument, but also relying on the relationships between them and the rest of the network. The network used to map subgroups of tweets is called actor-congruence network (Leifeld & Haunss 2012) and defines the links between tweets according to the arguments employed. This constitutes a measure of discursive similarity. The general idea is that the more concepts two tweets agree or disagree on, the more likely they are to belong to the same discourse coalition. It is composed by three elements: tweets (A = {a1, a2, … an}), concepts (C = {c1, c2, … cn}), and relationships (R =
{r1 for agreement, r2 for disagreement}). When tweet a1 talks about concept c1 in the same way as tweet a2, they have a relationship of agreement. When they talk about the same concept in different terms, the relationship is of disagreement. This network displays only relationships of agreement and is described by the following graph: