Bibliographic record
Abstract
This thesis presents the first sociophonetic analysis of vowel variation and change in Temiskaming Shores (population 9,920) and Kirkland Lake (population 7,981), two small communities in northern Ontario. I compare the speech of these two communities to that of Toronto, Ontario, the largest and most linguistically diverse city in Canada (population 2,731,571), and Thunder Bay, Ontario (population 107,909), a smaller urban centre 950 km northwest of Temiskaming Shores and Kirkland Lake and 1400 km northwest of Toronto. I analyze four vowel variables: Canadian Raising, the phonologically conditioned raising of the onset of front upgliding and back upgliding diphthongs /aɪ/ (as in the word price) and /aʊ/ (as in mouth) before voiceless consonants, the merger of the low back vowels /ɑ/ (as in lot) and /ɔ/ (as in thought), the Canadian Shift, the retraction and lowering of the front lax vowels /ɪ/ (as in kit) and /ɛ/ (as in dress) and /æ/ (as in trap), and the fronting of the high back vowel /u/ (as in goose). The data for the project comprises over 52,000 tokens of Canadian English vowels in 11 vowel categories drawn from two large corpora of Canadian English and based on a speaker sample that is stratified by community, age, and sex. I use linear mixed-effects regression models fit to Lobanov-normalized F1 and F2 of each vowel token to analyze the influence of the social factors of community, sex, and age, as well as the linguistic factors of following and preceding phonetic context on each phonological variable. The main findings of the thesis are that despite some regional differences, the vowel systems of Kirkland Lake and Temiskaming Shores are essentially similar to those of Toronto and Thunder Bay, and that this underlying stability corroborates the longstanding claim of homogeneity of English across Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".