Effects of phonemic variability and language dominance on Canadian French-English bilinguals? perception of French vowels in various phonological contexts
Bibliographic record
Abstract
Phonemic variability can cause productions within a category of one language to be mapped onto multiple categories in a different language (Escudero, 2009). For example, French productions of /ε/ are labeled both as /e/ and /ε/ by monolingual English listeners, likely due to spectral variability in production of French /ε/ (Strange, Levy, & Law, 2009). The present study examines how phonemic variability in Canadian French (CF) affects the perceptual tendencies of bilingual Canadian French-English listeners with varying levels of language dominance. Vowel productions by monolingual CF speakers were used in a modified identification task (Law, 2011). The vowels were word-final in several phonological contexts (preceded by labial, coronal, and back consonants; followed by labial and coronal consonants) in real and nonwords embedded in carrier phrases. A subset of these vowels /e-ε-i-y/ was analyzed for duration, vocalic midpoint, and formant trajectories to examine the relationship between phonemic variation (as a function of phonological environment) and the perception results by bilinguals dominant in either CF or Canadian English (CE). We predict that the performance of CE-dominant listeners will vary in terms of speed and accuracy based on how similar each token is to CE vowel category expectations.
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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".