Bibliographic record
Abstract
ABSTRACT: The Canadian Shift, a change‐in‐progress that is affecting the lax vowel subsystem of Canadian English, has been found to be active in a number of cities across Canada. Very little is known about the geolinguistic history and spread of the shift, however. Combining apparent time data from Thunder Bay, Ontario, with a comparison of lax vowel pronunciation in the speech of young people from both Thunder Bay and Toronto, the current study presents evidence against the hypothesis that the Canadian Shift has spread to Thunder Bay by way of a gravity model of diffusion. Although not identical, the vowel configurations are quite similar in the speech of young people from the two cities, and the apparent time findings suggest that the shift in Thunder Bay has not lagged behind the shift in Toronto. Results support the proposal that the English of Thunder Bay and Toronto share a common source and that the low back vowel merger, the pre‐cursor for the shift, was brought westward with the settlers to Thunder Bay in the late 19th and early 20th centuries. Subsequently, the Canadian Shift occurred simultaneously in both areas. Evidence of more urban features in the pronunciation of several Thunder Bay teenagers also raises questions about the future impact of mobility on the local dialect.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".