2. Goals  To advance understanding of the relationship between geographically-coded data and language data  To transform our notion of dialect and speech community based on geographical, demographic and social distribution of multiple features

3. Overview  We know that /æ/ is changing in this region and this time period. Question: How does that change spread over time and space?  Geo-social structures (the gravity model; Trudgill 1974) can trump straight-line geography (the wave model; Schmidt 1872),  Value addition with georeferenced social factors (Britain 2002; Lee & Kretzschmar 1993)

4. The issue of scale  The most important related work (e.g. Trudgill, Lee & Kretzschmar, Labov) has focused on vast areas — Grieve et al. use North America.  We start from this position: Language and social structure are local.  Use data that is more representative than ANAE and measure diphthongization

5. Neighborhoods by demographics x distance x linguistic features  Chambers and Trudgill (1998: 178ff): cross-city influence matrix  P =population of geographic center  d =distance between centers  S =index of linguistic similarity

6. Sociolinguistic Literature tell us …  Language varies …  As individuals speak to one another (locality)  Language is a brokered agreement between humans and used for various ends  Both within and across geographic domains (identity) (historical continuity) (translocational communication)  E.g., Blacks share markers with whites within a location differentiating them from Blacks elsewhere; yet, speakers often share pan-AAE markers

7. Geolinguistic Literature tells us …  Language varies …  By presence/absence of barriers (boundary conditions)  By sphere of influence to immediately smaller locations where similarity and status matters (gravity)  E.g., Chicago to Rockford and St. Louis  By large sweeping patterns where distance matters (wave)  E.g., CAUGHT~COT merger in US

8. Social Science Literature tells us …  Local knowledge varies …  In a rapidly decaying fashion (rapid decay)  E.g., there is a ‘nearness’ factor and not all data points have equal influence over each other  Multiple factors influence the spread (or not) of local knowledge (regressive covariation) (costly)  E.g., cost involved with transferring information regarding competition and cooperation

9. Features of Model  Locality, identity & historical continuity by community: geographic and social barriers  Gender, ethnicity, age, immigration, topography  Gravity & rapid decay: attraction by population centers within proximate range based on population  Regressive covariation & cost: varying weights and multiple solutions by location  Wave & measurable features: known markers that spread

10. Methodology

11. Speakers  20 speakers from WELS and DARE datasets  1870s: 2  1880s & 1890s: 4 + 2  1900s & 1910s: 4 + 2  1920s, 1930s, & 1940s: 3 + 1 + 2  16 Locations in WI

13. Idealized Model  This model accounts for regressive covariation and cost For some speech knowledge qua behavior in locale ℓ, K ℓ = β K1 S ℓ + β K2 G ℓ + ε K  K is a proxy for knowledge output (acoustic measures)  S ℓ = social factors  G ℓ = geographic factors

14. Society  Locality, identity & historical continuity by community: geographic and social barriers  Gender, ethnicity, age, immigration, topography S ℓ = β S1 F ℓ + β S2 E ℓ + β S3 log( L ℓ ) + β S4 log (W ℓ ) + ε K  F = % of population, foreign born in 1900  E = % of population, black in 1900  L = value of livestock in 1900  W = total manufacturing wages in 1900

15. Geography  Gravity & rapid decay: attraction by population centers within proximate range based on population  The features for the more geographic features can be stated similarly, as G ℓ = β K1 log( P ℓ ) + β K2 log (T ℓ ) + β K3 B ℓ + ε K  P = log (county population per sq. mi.)  T = log (time to Milwaukee per time to Minneapolis)  B = index of public or private transportation costs to MKE

20. Geographic Measures  Designed to capture gravity and decay  Population density  1900 population / sq mi in county  Measure of time of transportation  log(distance to Mke/distance to Minn) ℓ  Negative value is beneficial  Measure of manner of transportation  Number of ‘jumps’ in transportation type, and cost of transportation (0-3)  Private is more costly than public  Train is more costly than bus

21. Measure of Transportation Distance

22. So, what’s K ℓ ?  Ceteris paribus, presence or absence of regional markers  /æ/ class of words K ℓ = β K1 log( VD ℓ ) + β K2 log (F1N ℓ ) + β K3 log (F2N ℓ ) + β K4 log (TL ℓ ) + β K4 log ( Θ ℓ ) + ε K  Speaker variables  birth year  gender

23. Vowel Measures  Recordings of “Arthur the Rat”  Extracted from WELS/DARE recordings  Aligned TextGrid for Praat from Penn Aligner  Corrected edges of /ae/ and neighboring segments  Post processing selection  Pre-obstruent V > 40 msec in the front of the vowel space  /æ/ measures from Praat: VD=vowel duration, F1N=nucleus height, F2N=nucleus backness, TL=trajectory length, Θ =trajectory angle

25. Results

26. Preliminaries  Problem 1: acoustic similarity and grouping speakers with respect to birth year and gender  Problem 2: Covariance matrix for Geography  Problem 3: Covariance matrix for Society

27. K = Acoustic similarity  Cluster analysis on individual characteristics  First threw out a speaker because outlier on vowel height  New N = 19, but from one of the communities with two speakers  Clusters — but driven by birth year and gender  1. males of all ages  2. females born before 1900  3. females born after 1900

28. Preliminaries  Problem 1: acoustic similarity and grouping speakers with respect to birth year and gender  Problem 2: Covariance matrix for Geography  Problem 3: Covariance matrix for Society

29. Geographic measures  Recall: two gradient measures  Travel time differential to Milwaukee  Population density  Linear covariation near significant  R 2 = 0.15, p=0.056  One potential outlier; would make significant  Selected transportation time  Transportation captures density

30. Preliminaries  Problem 1: acoustic similarity and grouping speakers with respect to birth year and gender  Problem 2: Covariance matrix for Geography  Problem 3: Covariance matrix for Society

31. Society measures  Recall 4 measures  Urban class, rural class, ethnicity, immigration  Covary?  Rural class with urban class (R 2 = 0.19, p<0.05)  Rural covaries with transportation time (R 2 = 0.39, p<0.05); urban doesn’t  Immigrants with rural class (R 2 = 0.48, p<0.05) and urban class (R 2 = 0.41, p<0.05)  Ethnicity does not covary urban class or transportation

32. Revised (realistic) model  Dep var: Indiv acoustic measures  Ind vars: urban class + ethnicity + transportation time  Weight by speaker class (birth year, gender)

33. Not significant  Vowel backness  Vowel height  Angle of trajectory

34. Significant relation 1  Duration x urban social class

35. Significant relation 2  Trajectory length x transportation time

36. Whence straight-line distances?  Longitude is significant for vowel trajectory and almost for duration  Neither latitude nor longitude is significant for the other three measures  Interpretation  Bias toward westward settlement patterns  For eastward moving CAUGHT~COT expect inverse relation

40. Conclusions

41. Summary  Clarification of the broad sociolinguistic category of “geography  Parametric power: encodes distance and population  Reduces complex matrix of Chambers & Trudgill  Broadly reconceputalizes the notion of “geography” Lx measures = urban class + ethnicity + transportation time Weighted by age, gender  Keeps the focus local

42. Geographic influence on language variation?  Testing to see if georeferenced data is better than straightline distance  Knew this going in, but need to demonstrate this because current studies continue to ignore this  Some features do fall out by longitude (duration, trajectory length); how many other studies are due to source of change being at the statistical corner of the analysis space?  Transportation time should overcome this problem because it doesn’t matter which direction one comes from.

43. Future work  Convert county data to more local data (April 2, 2012??)  Will permit more robust GIS computation  Better treatment of biases  Ethnicity  Immigration  Geography  Build continuity with new data collections

45. Thanks! UW Graduate School The Dictionary of American Regional English Wisconsin Englishes Project (Luke Annear, Trini Stickle, Nick Williams)