Bibliographic record
Abstract
Taking as a point of departure the preliminary view of regional phonetic differentiation in Canadian English developed by the Atlas of North American English, this article presents data from a new acoustic-phonetic study of regional variation in Canadian English carried out by the author at McGill University. While the Atlas analyzes mostly spontaneous speech data from thirty-three speakers covering a broad social range, the present study analyzes word list data from a larger number of speakers (eighty-six) drawn from a narrower social range, comprising young, university-educated speakers of Standard Canadian English from all across the country. The new data set permits a more detailed view of regional variation within Canada than was possible in the Atlas, which focuses on differentiating Canadian from neighboring varieties of American English. This view adds detail to the established account in some respects, while suggesting a revised regional taxonomy of Canadian English in others. In particular, this article reports on several phonetic isoglosses that divide Canada's Prairie region from Ontario, thereby splitting the “Inland Canada” region of the Atlas into western and eastern halves. In fact, the data presented here suggest a division of Standard Canadian English into six regions at the phonetic level, rather than the three proposed by the Atlas: British Columbia, the Prairies, Ontario, Quebec (Montreal), the Maritimes, and Newfoundland. This taxonomy corresponds to the six major regions identified in the study of lexical data reported in Boberg (2005b).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".