Cross-dialectal exposure effects on the production and perception of the French low-vowel contrast
Bibliographic record
Abstract
Individuals’ speech patterns can change after extended exposure to a second dialect (D2) of their native language, even in adulthood. While speech production changes in D2 acquisition are well documented, less is known about changes in perception and how production and perception interact. The present study investigates these issues in relation to geographically mobile speakers of Quebec French and Hexagonal French (the French of continental France) by comparing them to non-mobile speakers of each dialect. A sentence elicitation task and a two-alternative forced choice (2AFC) lexical identification task were used to assess change in production and categorical perception of /a ~ ɑ/, a phonemic contrast that is widespread in Quebec French yet largely merged to /a/ in Hexagonal French. Results revealed no effect overall of mobility on /a ~ ɑ/ realization in production, but an asymmetrical effect of mobility on perception among the mobile Hexagonal French participants. Additionally, a complex interplay was observed between changes to perception and production, challenging the notion of a direct link between these domains in D2 acquisition. These findings present evidence of post-adolescent, perceptual adaptation and suggest that such perceptual adaptation is not linearly associated with production-based changes in D2 acquisition.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".