Putting “the Other Maine” on the Map: Language Variation, Local Affiliation, and Co-occurrence in Aroostook County English
Bibliographic record
Abstract
This dissertation examines Aroostook County English, an understudied variety spoken at the border of Northern Maine (USA) and Western New Brunswick (Canada). The analysis is based on a socially stratified corpus of sociolinguistic interviews with 30 locals and word list data from the same individuals. The project has two goals: First, to establish how the variety of English in this area fits into the surrounding dialect regions, and second, to determine whether speakers who closely align themselves with local values share a similar sociolect. Examining eight phonological features that have traditionally been used to distinguish different varieties of English in the area (namely, rhoticity, the LOT-THOUGHT merger, the NORTH-FORCE merger, the MARY-MARRY-MERRY merger, broad-a in BATH, intrusive-r, fronted START, and fronted PALM, see Stanford, 2019), I find that Aroostook County English includes a mix of features from Eastern New England and Atlantic Canada, suggesting it is a transition zone (see Chambers & Trudgill, 1998). It also challenges previous accounts of linguistic homogeneity within Northern Maine (Kurath, 1939: 17), demonstrating that there is more linguistic variation within the state than previously assumed. Focusing on three morphosyntactic variables (was leveling, demonstrative them, and preterite come), this dissertation finds that speakers with high local affiliation tend to have more nonstandard variants in their repertoire. However, speakers with ties to the education system do not conform to this pattern and usage rates vary widely. This suggests that the combination of nonstandard was, them, and come indexes high local affiliation for some speakers, but their absence does not imply the opposite. I conclude that the combination of nonstandard variants has a different social meaning from their absence, similar to the way that variants of the same variable can have non-complementary connotations (Campbell-Kibler, 2011). Taken together, these findings highlight the usefulness of linguistic data from small, remote communities for understanding the development of the English language and the relevance of identity-based factors for understanding patterns of language variation and change (see Hazen 2002; Tagliamonte, 2006c, 2013, 2017).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".