Perception of Canadian French word-final vowels in lexical and morphosyntactic minimal pairs by English learners of French.
Bibliographic record
Abstract
The present study investigated perception of Canadian French word-final vowels in various phonetic contexts by English-dominant bilinguals living in Montreal. In a modified identification task, listeners selected responses that rhymed with the target words; production of each response corresponded to a target vowel (e.g., response “ai” is pronounced /e/). Target words included real and nonsense words contrasting in word-final /i e ε a o u y o// and morphosyntactic verb minimal pairs contrasting in word-final /e-ε/ (e.g., first-person singular future “parlerai” versus conditional “parlerais”) embedded in carrier sentences. Of interest were the perception patterns of /e/ and /ε/. Both are phontactically allowed word-final in Canadian French, but not in English. Also, previous studies suggest that /y/ and /u/ could have context-dependent perceptual patterns for English listeners. Results showed that bilingual listeners performed well overall and were at ceiling for control vowels. Most mistakes were /e-ε/ confusions, though participants were above chance, with highly proficient French bilinguals making fewer errors. /y/ and /u/ were accurately identified across contexts, suggesting that /y/ and /u/ perception was not particularly difficult in this study. Reaction time differences, mouse tracking patterns, and effects of real versus nonsense words will also be discussed. [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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".