Maintenance of /e-ε/ in word-final position as a phonemic and morphemic contrast in Canadian French.
Bibliographic record
Abstract
Many dialects of French have merged /e-ε/ to /e/ in word-final context. The present study investigated the stability of this contrast in Canadian French. Productions of four Canadian French-dominant speakers (two monolingual, two bilingual) were recorded and analyzed. Real (e.g., “thé”) and nonsense (e.g., “gispais”) words ending with /e/ or /ε/ were used, as well as real and nonsense verbs distinguished morphosyntactically by the same vowel contrast (e.g., first person singular future “parlerai” versus first person singular conditional “parlerais”), all embedded in carrier sentences. Results showed that participants maintained spectral and duration distinction for the vowel contrast when preceded by labial, coronal, and back stops in real and nonsense words. All participants showed varying degrees of coarticulation for /e/ when preceded by /r/; /e/ was spectrally lower than in other contexts. Three of the four participants maintained a stable distinction in morphosyntactic context: monolinguals exhibited the best retention of the distinction, whereas the bilinguals had partially or completely overlapping distributions, merging /e-ε/ to /ε/, not /e/. These patterns suggest difficulties in perception of this contrast for English late L2 learners of French, due to the fact that this contrast is not phonotactically possible in English. [Work supported by NIH F31DC008075.]
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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 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".