Perception of Canadian French word-final vowels by English-dominant and French-dominant bilinguals
Bibliographic record
Abstract
Self-identified English-dominant and French-dominant bilinguals from Montreal participated in a modified vowel identification task. Group differences in accuracy and speed for identifying experimental vowels /e, ε, o, u, y, ⊘/ were investigated relative to control vowels /i, a/, expected to be easiest and fastest to identify by both groups. Of interest was the performance on front-rounded /y-⊘/ (non-phonemic in English) and /e-ε/ (phonologically contrastive in both languages, but // is disallowed word-finally in English). Both groups performed well overall in identifying experimental vowels although the French-dominant group was comparatively more accurate and faster. The English-dominant group was slower than the French-dominant group in identifying /y/ and /e/. Mouse cursor movements captured trial-by-trial revealed that both groups often moved the cursor toward the response button for /u/ before correctly identifying /y/. Results showed that English-dominant participants demonstrated less-automatic perception of most experimental vowels. However, performance speed and mouse patterns of the French-dominant group varied among native vowel categories, implying possible interactions between automaticity and auditory salience. Productions of /e-ε/ by participants were analyzed to explore the relative robustness in production of this contrast among participants. Correlations between task performance and measures of French proficiency are explored. [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 | 0.001 | 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) | 0.005 | 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".