Dialect development in Nain, Nunatsiavut: emerging English in a Canadian aboriginal community
Bibliographic record
Abstract
This dissertation is a case study of the English spoken in Nain, Nunatsiavut (Labrador), an Inuit community in northern Canada. Conducted within a variationist sociolinguistic framework, it offers a quantitative analysis of a majority language as spoken in an Aboriginal community, an understudied area of research. Nain is an ideal location for this type of study because Labrador Inuit are experiencing rapid language shift as the population becomes predominantly English speaking, with few people learning Inuttitut as their native language, creating an opportunity to examine an emerging variety of English. In this dissertation, I contrast Nain Inuit English with the variety spoken in Newfoundland, the English-speaking region with which residents have historically had contact. I survey three sociolinguistic variables that typify Indigenous English and/or Newfoundland English—one phonological (the realization of interdental fricatives, e.g., this thing pronounced as dis ting), one morphosyntactic (verbal -s, e.g., I loves it), and one discourse (adjectival intensification, e.g., very happy vs. really happy vs. so happy)— to test notions of diffusion and transmission while also looking for evidence of transfer from Inuttitut. I also consider theories of new dialect formation and models of postcolonial English and how they apply to Nain. Complicating this comparison is the fact that some interviewees overtly self-identify as not being Newfoundlanders, raising the possibility that they may try to avoid Newfoundland English variants. Results indicate that Nain Inuit English shares some traits with the English spoken in the rest of the province but has also developed in different ways, though few of these differences can be attributed to influence from Inuttittut. This study also contributes to the growing body of work on majority languages in indigenous communities, in addition to deepening our understanding of English in Labrador.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".