Mapping Dialect: GIS Applications in Studying Dialect of Western Pennsylvania Ayad Y. and Luthin H
Mapping Dialect: GIS Applications in Studying Dialect of Western Pennsylvania
Ayad, Y. ([email protected]) Luthin, H. ([email protected]) Clarion University of Pennsylvania
Abstract Historically, most surveys of American dialect have focused on large‐scale regions of the country: the Linguistic Atlas of New England, the Linguistic Atlas of the Middle and South Atlantic States, and the Linguistic Atlas of the Gulf States, for example. However, even in a region as small and relatively homogeneous as western Pennsylvania, there is measurable diversity of language. This paper describes a dialect survey, in progress for the last 10 years that focuses precisely on dialect variation within western Pennsylvania. In recent years, the researchers have tapped the potential of GIS techniques to help make these distributions visible and assess the underlying speech patterns of the region. This paper is an attempt in this direction. It demonstrates the methods and techniques used to manipulate the collected data and the process of creating visual cartographic representations of the results.
Introduction The essence of dialectology is the study of variation in language. Variation can be found at every level of language, from the nuances of pronunciation and the details of verb conjugations, to the structure of sentences, the content of the vocabulary, and patterns of interaction between individuals. The field of dialectology today is pursued down all of these avenues and more, and from a multitude of perspectives (social, anthropological, diachronic, synchronic, longitudinal, qualitative, quantitative, statistical), but it had its origins in the study of regional variation—the geography of language—and cartography was at the heart of its enterprise.
The earliest systematic exploration of American dialects was undertaken for the American Dialect Society by Hans Kurath, who published the three‐volume Linguistic Atlas of New England in 1939.1 This, and the eight other regional atlases that followed it over the course of the next 40 years, established a base‐line portrait of the dialect areas of the United States. Since then, other major studies have been produced, most notably the Dictionary of American Regional English,2 out in four volumes to date (from A‐Sk), and the definitive Atlas of North American English.3 The cartographic presentation of data is a key part of both these publications.
1 Kurath, et al. 1939‐43. Linguistic Atlas of New England. 6 vols, bound as 3. Providence: Brown University for the American Council of Learned Societies. 2 Cassidy & Hall (eds). 1985‐2002. Dictionary of American Regional English, vols. I‐IV. Cambridge: Harvard U. Press. 3 Labov, Ash & Boberg. Atlas of North American English: Phonology and Phonetics. Berlin: Mouton/de Gruyter. 2006.
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Mapping Dialect: GIS Applications in Studying Dialect of Western Pennsylvania Ayad Y. and Luthin H
The present study is based on the Western Pennsylvania Dialect Survey, an online database hosted by the English Department at Clarion University.4 The WPDS began as a teaching tool for a class on American dialects, and its narrow focus is a by‐product of the demographics of the student body at Clarion University, where the vast majority of students are from the region itself. There were simply not enough representatives of outside dialects to conduct meaningful nation‐wide surveys. To salvage the field‐collecting experience for the students, the teacher of that class (Luthin), turned his attention to micro‐variation within the Western Pennsylvania sprachbund. To this end, students consult the language use in their own towns and cities and come up with a list of items that display variation. The current version of the survey, which has been online and collecting data since 2003, consists of 60 such variables—mostly vocabulary, but also including some phonological variables and a few syntactic alternants as well.
Among these variables are many of the more prevalent Western Pennsylvania regionalisms, such as redd up vs. clean or tidy up; sledding vs. sled riding; spicket or spigot vs. faucet; the pronunciation of the word color as either “keller” or “kuhler”; the use of yinz as a second person plural pronoun; and the needs fixed syntactic pattern, which exists in opposition to either the Standard English needs to be fixed or the Southern variant needs fixin’. Other variables are more localized to Pittsburgh and its environs—the so‐ called “Pittsburghese” dialect—for instance, gumband vs. rubberband; nebby vs. nosey; slippy vs. slippery; and the monophthongization of the /aw/ vowel in words like out and downtown as “aht” and “dahntahn”.
Prior to 2003, students composed their maps the hard way: by hand, working with small samples of the data they collected. With the increasing availability of mapping software in the University’s GIS lab, however, another possibility suggested itself. A small change to the database allowed us to collect zip code data along with the other personal information associated with each record. From 2003 on, then, each response in the database could be associated with a point on a map, allowing us for the first time to take a really close look at the spatial patterns contained in our data. There are indeed some straightforward geographic distributions for several of our variables. For instance, the isogloss boundary between the Northern and North Midlands dialect areas (which runs across the upper part of the commonwealth, splitting off the northernmost tier of counties from the rest of Pennsylvania) is plainly visible on a number of maps: on the REDD UP map, for example. But it is also fair to say that, for the majority of our variables, while there may be tantalizing spatial tendencies, the distributions are anything but straightforward. This is perhaps to be expected for the kind of “intramural” variation we are exploring with this survey. The question is why. Are most variants simply in random circulation throughout the speech area, or are there other factors in play that work to obscure existing patterns?
The fact is, not all patterns are necessarily spatial in character. There are a host of social factors that influence speech every bit as strongly as geography does. Socioeconomic class and level of education, for instance, have powerful effects on the way people speak, as do age and gender, ethnicity and occupation. Sometimes variables are confined to a particular class or gender or ethnicity; others are strongly associated with a particular age‐group, or can increase or decrease in prevalence or intensity
4 The WPDS site can be accessed at: http://web2.clarion.edu/english‐luthin/dialect2003/
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Mapping Dialect: GIS Applications in Studying Dialect of Western Pennsylvania Ayad Y. and Luthin H with age; still others might covary with level of education. Often all these factors can interact, cross‐ influencing each other, while spreading geographically as well. The needs fixed construction, for instance, is found throughout Western Pennsylvania, and cuts across socioeconomic class, largely without stigma. The keller pronunciation, on the other hand, and the use of the non‐standard pronoun yinz, are strongly disfavored by upper and upper‐middle class speakers, and are inversely associated with level of education.
Sometimes the interactions among all these factors can be dauntingly complex. Like keller and yinz, the Pittsburgh pronunciation pattern found in words like down and out (“dahn” and “aht”), is correlated with both class and education. In addition, it is favored more by men than by women, particularly men associated with blue‐collar occupations like (in the old days of Pittsburgh steel) millwork, carpentry, and manufacturing. It is still a prominent feature of the Pittsburgh speech‐scape, but younger speakers are less likely overall to use the variable than older speakers are.5 The pronunciation also appears to have been spreading outward from the city into surrounding areas, so there is a spatial component to this variable as well. Later in the paper, we will look more closely at this variable to see how mapping can help tease apart its intricacies.
5 For a detailed recent discussion of this variable, see Johnstone, Bhasin & Wittkofski (2002), “ ‘Dahntahn’ Pittsburgh: Monophthongal /aw/ and Representations of Localness in Southwestern Pennsylvania,” American Speech, 77:2, pp. 148‐166.
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Mapping Dialect: GIS Applications in Studying Dialect of Western Pennsylvania Ayad Y. and Luthin H
Survey Data Description The survey results were presented in Excel format; it was converted to a file geodatabase table in order to simplify the parsing process. Out of the available 60 different topics, 19 were identified and extracted based on their significance. Those were tabulated (see Appendix A) and a list of other attributes that might have a possible correlation with those topics was listed (Table 1). Those attributes are: Population Density, Community Type as a child (ENVIRONS_CHILD) and their Socioeconomic Class category at childhood time (CLASS_CHILD), the Gender (SEX) and the Age (AGE) of the respondent. The present study is concerned with the methodology involved in extracting those data and presenting them in a spatially sound format in order to visually identify geographic correlations and interrelationships. Therefore, only one topic was considered as an example of data extraction (DOWNANDOUT).
Table 1 List of the Selected Western Pennsylvania Dialect Survey Topics
Suggested Possible # Topic Attribute Abr. Attribute Correlations Pop_Density ENVIRONS_child CLASS_child Sex Age 1 1 sled riding SLEDDING SLD 2 3 gumband GUMBAND GUM population/community type X X
3 4 nosey/nebby NEBBY NEB 4 5 redd up REDDUP RED 5 6 fill/feel FILL_FEEL FIL class X 6 8 dahn‐and‐aht DOWNANDOUT DOW population/community type, X X X X class, gender
7 9 yinz YINZ YIN community type, class X X 8 11 Don/Dawn DON_DAWN DON class X 9 13 bucket/pail BUCKET_PAIL BUK age X 10 16 nauseous NAUSHOUS NAU age, gender X X 11 21 needs Xed NEEDS_XED NEE class X 12 22 “school bus” SCHOOLBUS SCH class, age X X 13 23 stop light STOPLIGHT STO class X 14 25 slippy SLIPPY SLP 15 26 firefly FIREFLY FIR age X 16 29 thorns (large) JAGGERS_LARGE JGL 17 30 thorns (small) JAGGERS_SMALL JGS 18 39 yankee bump YANKEEBUMP YAN 19 41 boonies BOONDOCKS BOO 20 45 whenever WHENEVER WHN age X 21 49 chipped ham CHIPCHOP CHP 22 51 maple twirlers MAPLESEED MAP 23 53 let/left LETorLEAVE LET
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Mapping Dialect: GIS Applications in Studying Dialect of Western Pennsylvania Ayad Y. and Luthin H
Project Objectives This is an exploratory project that presents an exercise in mapping dialect survey responses and investigates their spatial distribution significance. In order to achieve an acceptable set of recommendations for similar future projects the following objectives and suggested:
‐ To explore different methods for effectively extract and cartographically represent the given dialect topics and their possible correlation with the other demographic, cultural and socio‐ economic variables. ‐ To trace recommendations on future data collection, both in the type of dialect topics and the design of the survey database ‐ To explore the spatial relationships between a set of given dialect topics ‐ To explore the correlation between the given dialect topics and other demographic, cultural and socio‐economic variables
Methods It is important to mention that only those zip codes of Pennsylvania and the surrounding states were considered in this study (393 zip codes (86%) out of a total of 456 unique ones) in order to optimize the analysis time without hindering the quality of the results. Only 14% of the unique zip codes had their original child zip code (ZIP_CHILD) outside the study area, those records were spread all over the United States and were omitted from the analysis.
In order to obtain the number of respondents per each topic and topic answer for every unique zip code, several summary tables were created after extracting every topic answer at an earlier stage (Process “A. Preparation” in Figure 2). New fields were added (with unique filed name for every topic answer) and were filled with the counts that were calculated in from the “Summary Statistics” analysis function (Frequency). Those tables were then cleaned from unnecessary fields (“Frequency” and the summary statistics fields) and finally joined with the Zip Code point feature class (process “B. TOPIC” in Figure 2). The final result is the Zip Code feature class attribute table populated with fields that hold
Figure 1 Distribution of the Respondent Unique Zip Code Points Page 5 of 26
Mapping Dialect: GIS Applications in Studying Dialect of Western Pennsylvania Ayad Y. and Luthin H information about the counts of respondents for each unique answer (for a selected topic) at each corresponding zip code point.
The Model Builder was mainly used to automate most of the required tasks. Many of the topics and their answers had to be extracted from the main survey results table. Matters got more complicated when attempting to extract the topic answers in relation to other variables such as the community type (urban, suburban …etc) or class category (rich, poor …etc.) that the respondent was raised in as a child, or his/her gender and age. In Figure 2, processes ”C. ENVIRONS”, “D. CLASS”, and “D. SEX”, are examples of how the model can be expanded to include the extraction of the given topic answers in relation to those variables. In this matter, the model builder has helped tremendously in reducing the time that would otherwise be used in manually parsing those results and linking them to the corresponding zip‐ code point feature class.
A. PREPARATION B. TOPIC
C. ENVIRONS
C. CLASS
D. SEX
Figure 2 Sample model diagram extracting the "DOWNANDOUT" topic and its corresponding variables (ENVIRONS, CLASS, and SEX)
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Mapping Dialect: GIS Applications in Studying Dialect of Western Pennsylvania Ayad Y. and Luthin H
The number of fields created to represent each topic and its corresponding values from the other variables can be numerous depending on the number of answers and the number of values in each of the variables. For example, the resulting zip code feature class attribute table of the extracted topic “DOWNANDOUT” had 28 new fields. Two fields, one for each of the answer values (Table 2), Twelve for the combination of those two values with the corresponding community type (ENVIRONS_CHILD) of Table 3, Ten for the combination of the same fields with the corresponding class type (CLASS_CHILD) of Table 4, and four for their combination with the gender information (SEX).
Table 2 Topic "DOWNANDOUT" answers and number of respondents
DOWNANDOUT FREQUENCY The "ah" sound, as when the dentist tells you to "Say Ah." 166 The "ow" sound, as in "Ow! I hurt myself." 1009
Table 3 Possible Community Type values and their overall respondent counts
ENVIRONS_CHILD FREQUENCY Large Town 38 Rural 330 Small City 114 Small Town 368 Suburban 260 Urban 76
Table 4 Possible Class type values and their overall respondent counts
CLASS_CHILD FREQUENCY lower class (welfare) 19 middle class (blue collar) 567 middle class (white collar) 406 upper class (wealthy) 28 working class (poor) 166
Parsing the Survey Data It is important to mention that the survey was not designed to be included in any future GIS or database analysis, which presented, in several cases, some challenges in data cleanup and re‐organization. The data was received in an Excel spreadsheet; it included many variables, and contained some inconsistencies in the attribute values. In many cases, for example, the respondent chooses not to fill specific entries such as gender or year of birth. In other cases, he/she would also leave some answers
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Mapping Dialect: GIS Applications in Studying Dialect of Western Pennsylvania Ayad Y. and Luthin H blank which added a value of “Choose one…” or blank lines “_____” as it was previously designed in the online form default field values.
A series of models were built adopting the same methods and were used in extracting and parsing all other topics and their corresponding attributes as needed.
For representation purposes and for future analysis possibilities, those zip codes that carried respondent counts were converted into a seamless raster surface using the “Density” function of spatial analyst in order to interpolate the number of respondents and visualize the extents of their spatial distribution.
Results Overall, the majority of respondents in the study area were women (66.1% ‐ Figure 3). The spatial distribution of those suggests major concentrations around Pittsburgh, Clarion, Erie and their surrounding areas (Figure 4).
Figure 3 Percentages of the Overall Gender (SEX) Responses
Furthermore, the respondents’ answers to describe their childhood community type (ENVIRONS_CHILD ‐ Figure 5) resulted in 31% raised in a small town community, while most of the other majority were either in rural (27.8%) or suburban communities (21.9%).
The spatial distribution of the community type responses suggests little differentiation between what is “rural” versus “Small Town” community; both of those were concentrated mainly around Clarion and the surrounding counties (Figure 6). The suburban communities, on the other hand, were mainly concentrated around the Pittsburgh area with few distributed in Erie in the north.
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Mapping Dialect: GIS Applications in Studying Dialect of Western Pennsylvania Ayad Y. and Luthin H
Figure 4 Gender Spatial Distribution for All Respondents in the Study Area
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Mapping Dialect: GIS Applications in Studying Dialect of Western Pennsylvania Ayad Y. and Luthin H
Figure 5 Percentages of the Overall Community Type (ENVIRONS_CHILD) Responses
Finally, the majority of the respondents (82%) considered their childhood to fall in the middle‐class categories (47.8% blue collars and 34% white collars), most of the others were categorized as working class (14%) and very few in the lower and upper class categories (Figure 7).
The spatial representation of the class category responses clearly shows a more homogenous “blue collar” middle class distribution around both Clarion and Pittsburgh areas, while a distinctive pattern of the “white collar” middle class concentrates mainly around Pittsburgh and the surrounding regions, notable concentrations also exist in the Erie area (Figure 8).
Topic: “DOWN AND OUT” The second set of results shows the distribution of the “DOWNANDOUT” topic. Two possible values were mapped: “The "ah" sound, as when the dentist tells you to "Say Ah."” (“AH” Sound) And “The "ow" sound, as in "Ow! I hurt myself."” (“OW” Sound). Figure 9 shows that significantly lower numbers of respondents have the “AH” Sound than the “OW” Sound. The latter is more dominant to the northern parts of western Pennsylvania. The “AH” Sound obviously is being used in the Pittsburgh area more than anywhere else in the study area, this is strengthened by the spatial distribution in correlation to the community type of the respondent childhood (Figure 10), where fewer respondents originated from more rural areas and more of them were from urban and suburban areas. Figure 11, on the other hand, shows greater concentrations of the “OW” Sound over the rural, small town and suburban areas more than any other community type. Respondents recorded their answers in this category more than the former one. In addition, as mentioned earlier in the introduction, the middle class (blue collar) scored the highest for the “AH” Sound usage which was concentrated mainly around the Pittsburgh area (Figure 12), it was also noticeable for the middle class “white collar” and the working class “poor”. Those three class categories were also prominent for the “OW” Sound more than the upper class “wealthy” and the lower class “welfare” which spread to cover most of the populated area in western Pennsylvania (Figure 13).
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Mapping Dialect: GIS Applications in Studying Dialect of Western Pennsylvania Ayad Y. and Luthin H
Figure 6 Community Type Spatial Distribution for All Respondents in the Study Area
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Mapping Dialect: GIS Applications in Studying Dialect of Western Pennsylvania Ayad Y. and Luthin H
Figure 7 Percentages of the Overall Class (CLASS) Category Responses
Furthermore, the “AH” Sound is concentrated more in the Pittsburgh area which is shown in the Gender Distribution Figure 14 and Figure 15. Comparing both figures shows a prominent characteristic of the female population in the Pittsburgh area to say the “AH” Sound while their use of the “OW” Sound is more persistent across most of western Pennsylvania. The same trend is mirrored with the male population but a more articulate separation exists between the “AH” Sound and the “OW” Sound usage: The latter is weakly used in the male population around the Pittsburgh area (Figure 15) while the former is seldom used outside the same area (Figure 14).
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Mapping Dialect: GIS Applications in Studying Dialect of Western Pennsylvania Ayad Y. and Luthin H
Figure 8 Class Type Spatial Distribution for All Respondents in the Study Area
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Mapping Dialect: GIS Applications in Studying Dialect of Western Pennsylvania Ayad Y. and Luthin H
Figure 9 Overall Spatial Distribution of the "DOWNANDOUT" Topic
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Mapping Dialect: GIS Applications in Studying Dialect of Western Pennsylvania Ayad Y. and Luthin H
Figure 10 The "AH" Sound Spatial Distribution in Relation to the Community Type Page 15 of 26
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Figure 11 The "OW" Sound Spatial Distribution in Relation to the Community Type Page 16 of 26
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Figure 12 The "AH" Sound Spatial Distribution in Relation to the Class Category Page 17 of 26
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Figure 13 The "OW" Sound Spatial Distribution in Relation to the Class Category Page 18 of 26
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Figure 14 The "AH" Sound Spatial Distribution in Relation to Gender Page 19 of 26
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Figure 15 The "OW" Sound Spatial Distribution in Relation to Gender Page 20 of 26
Mapping Dialect: GIS Applications in Studying Dialect of Western Pennsylvania Ayad Y. and Luthin H
Lessons Learned It was a unique and challenging exercise to work with varied and diverse issues in mapping the dialect of western Pennsylvania. The following lessons are earned from this experience and are highly recommended to be taken into consideration when conducting similar studies:
‐ Process automation through ArcGIS Model Builder or other tools (scripting & programming) is essential; the number of iterations that were carried out in this study in order to modify functions and refine results was endless, the use of models helped alleviating the pain in the iteration process and assisted in the organization of ideas and brainstorming for possible methods. ‐ Although using the Model Builder reduced the amount of time for iterations but running one model would take a significant amount of time especially in the Joining Table process (in the case of DOWNANDOUT 28 different tables and their attributes were joined to the zip points feature class); It is recommended to adopt common sense practices in optimizing the model, from selecting only those zip code points that are pertinent to the topic/variable under consideration and storing all of the data in a file geodatabase which proved to slightly improve the performance. ‐ The effective cartographic representation of multiple topic and their correlated variables is a challenging task; this study explored only limited ways of representations, other methods are encouraged (complex customized point symbology/size, using zip code areas instead of points, classified raster surfaces, and other statistical methods …etc.) ‐ The survey data was limited to a group of students in a classroom setting; a wider range of population and time for data collection are believed to contribute to better representation and mapping out variations.
Future Directions It would be interesting to explore the possibilities of using Spatial Analyst in depth in order to analyze the relationship patterns between each topic and its corresponding suggested correlations. This could be done by assigning class values for density maps (or interpolated ones) of both the topics and the associated variables, weighted with the counts of each topic per zip code location, then try to find the spatial correlations between both density maps. Some correlation could be extracted using a scatter plot or other statistical analysis after assigning “NoData” to all zeros in the raster images (the dominant value).
Interpolation of the zip points instead of using the density methods in calculating the continuous raster surface might also be considered. The Density function takes into consideration the aggregation and the dispersion of the zip code points besides the weighting factor or the number of respondent per point. An interpolation method such as the Inverse Distance Weighted (IDW) might give a better interpolation to the number of respondents per zip code more than describing the spatial distribution of the points.
Refining the models and include some scripts as needed in order to facilitate the entry and the specification of the required topic and variables from a dialogue box instead of tweaking the model every time a new study is required. This can be started using the model builder and could be extended
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Mapping Dialect: GIS Applications in Studying Dialect of Western Pennsylvania Ayad Y. and Luthin H to build custom application using ArcObjects in order to give multiple options of parsing and mapping the suggested topics with their corresponding variables.
Evaluate the results of the suggested models to existing dialect maps of the United States and draw comparisons of differences and similarities. Consulting other studies, national and international, might reveal better methods to analyze such a complex setting of variables and might help in their cartographic representation.
Finally the inclusion of the respondent Age or Age Group and Population Density as secondary variables would be of a good interest to complement the current efforts.
Dr. Yasser Ayad [email protected] Associate Professor, GIS Department of Anthropology, Geography and Earth Sciences Clarion University of PA 365 Science and Technology Center Clarion PA 16214 814.393.2990 (Voice)
Dr. Herb Luthin [email protected] Professor of English English Department Clarion University of PA 205B Davis Hall Clarion PA 16214 814.393.2738 (Voice)
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Mapping Dialect: GIS Applications in Studying Dialect of Western Pennsylvania Ayad Y. and Luthin H
Appendix A Topic Values and their Descriptions
ABR. TOPIC VALUES DESCRIPTION VOCABULARY: Verb; to coast down a snow‐
SLD SLEDDING covered slope on a sled sled riding sledding VOCABULARY: Noun; a short loop of rubber or
GUM GUMBAND latex used to hold multiple objects together gumband rubberband VOCABULARY: Adjective; overly inquiring,
NEB NEBBY curious about other's affairs a neb a neb‐nose a nib a nib‐nose a nosey parker nebby nibby nosey VOCABULARY: Verb; to tidy up or clean a messy
RED REDDUP room or office clean up pick up redd up straighten up tidy up PRONUNCIATION: Homophony of long and short
FIL FILL_FEEL /i/ before syllable‐final /l/ Not at all Pretty close! Yes they do PRONUNCIATION: Monophthongization of the
DOW DOWNANDOUT /aw/ diphthong The "ah" sound, as when the dentist tells you to "Say Ah." The "ow" sound, as in "Ow! I hurt myself." VOCABULARY: Use of yinz (“you ones”) as 2nd
YIN YINZ person plural pronoun No, never. Sometimes.
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Mapping Dialect: GIS Applications in Studying Dialect of Western Pennsylvania Ayad Y. and Luthin H
ABR. TOPIC VALUES DESCRIPTION Yes, all the time. PRONUNCIATION: Merger of “ah” and “aw” (the
DON DON_DAWN low back and mid back lax vowels) No, they don't Yes, they do VOCABULARY: Nouns; focuses on the BUK BUCKET_PAIL interchangeability or differentiation of the words bucket and pail I use "bucket" for all types I use "pail" for all types. I use "bucket" and "pail" interchangeably. A bucket is a special type of pail. A pail is a special type of bucket. VOCABULARY: The word nauseous can be NAU NAUSHOUS pronounced in different ways in American English I use another word entirely Nauseous (3 syllables; pronounced with a "z" sound) Naushous (2 syllables; pronounced with a "sh" sound) SYNYAX: Structure of the verbal complement of NEE NEEDS_XED need (infinitive be vs. past participle vs. progressive participle) needs to be washed needs washed needs washing PRONUNCIATION: Syncope of syllable‐final /l/,
SCH SCHOOLBUS especially before a consonant Barely. No. Yes. VOCABULARY: Noun; variant terms for traffic
STO STOPLIGHT signals at intersections redlight stoplight traffic light traffic signal VOCABULARY: Adjective; describing a surface SLP SLIPPY made dangerous to foot and vehicle traffic by ice, grease, or water Neither slippery slippy VOCABULARY: Noun; competing terms for a
FIR FIREFLY bioluminescent beetle of family Lampyridae I say both. firefly lightning bug
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Mapping Dialect: GIS Applications in Studying Dialect of Western Pennsylvania Ayad Y. and Luthin H
ABR. TOPIC VALUES DESCRIPTION VOCABULARY: Noun; regional designation for
JGL JAGGERS_large thorns when they are large in size jaggers prickers stickers thorns VOCABULARY: Noun; regional designation for
JGS JAGGERS_small thorns when they are small in size jaggers prickers stickers thorns VOCABULARY: Noun; terms for a sudden small YAN YANKEEBUMP dip or rise in the road that momentarily lifts or drops passengers in a moving vehicle I don't use any of these cherry bump thank‐you‐mam yankee bump VOCABULARY: Noun; terms for small, out‐of‐the‐
BOO BOONDOCKS way towns or rural areas B.F.E. NONE OF THE ABOVE boondocks boonies cuts hickville jingweeds
SYNTAX: Replacement of all uses of the
WHN WHENEVER subordinating conjunction when with whenever
No Yes VOCABULARY: Adjective; terms for very thinly
CHP CHIPCHOP sliced meats, especially ham chip‐chopped ham chipped ham thin‐sliced ham VOCABULARY: Noun; terms for the flying seeds
MAP MAPLESEED of many species of maple tree (maple) fliers (maple) helicopters (maple) spinners (maple) twirlers (maple) wings NONE OF THE ABOVE
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Mapping Dialect: GIS Applications in Studying Dialect of Western Pennsylvania Ayad Y. and Luthin H
ABR. TOPIC VALUES DESCRIPTION
VOCABULARY: Verb; Use of the verb leave as a
LET LETorLEAVE replacement for the permission‐granting verb let
LEAVE LET
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