Toronto English vowels: Deriving the vowel space from conversational data.
Bibliographic record
Abstract
This presentation develops an overview of the Toronto English vowel space using data derived from the unscripted, casual speech typical of sociolinguistic interviews, rather than the more “formal” avenue of laboratory speech. Drawing on a corpus of sociolinguistic interviews collected by Walker & Hoffman (“language contact, linguistic variation and ethnic identity in Toronto English” SSHRC SRG 410-2008-2048) and using multiple-point formant measurements of stressed vowels in “clean” environments (e.g., avoiding coda nasals and liquids), this study develops views of the vowel space that illustrate modal or average vowel centers, within-category scatter, between-category dispersion, and VISC. In refining this kind of vowel data collection and analysis, this research provides a baseline for further studies, such as to describe vowel differences in “ethnically” marked varieties of English, to characterize adjustments made in specific phonological contexts, to identify previously undescribed phonetic variation in Canadian English, and to compare similar data from other varieties of English.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".