Perceptual Discrimination of Phonemic Contrasts in Quebec French: Exposure to Quebec French Does Not Improve Perception in Hexagonal French Native Speakers Living in Quebec
Bibliographic record
Abstract
In Quebec French, /a ~ ɑ/ and /ε ~ aε/ are phonemic, whereas in Hexagonal French, these vowels are merged to /a/ and /ε/, respectively. We tested the effects of extended exposure to Quebec French (QF) as a second dialect (D2) on Hexagonal French (HF) speakers’ abilities to perceive these contrasts. Three groups of listeners were recruited: (1) non-mobile HF speakers born and living in France (HF group); (2) non-mobile QF speakers born and living in Quebec (QF group); and mobile HF speakers having moved from France to Quebec (HF>QF group). To determine any fine-grained effects of second dialect (D2) exposure on the perception of vowel contrasts, participants completed a same–different discrimination task in which they listened to stimuli paired at different levels of acoustic similarity. As expected, QF listeners showed a significant advantage over the HF group in discriminating between /a ~ ɑ/ and /ε ~ aε/ pairs, thus suggesting an own-dialect advantage in perceptual discrimination. Interestingly, this own-dialect advantage appeared to be greater for the /ε ~ aε/ contrast. The QF listeners also showed an advantage over the HF>QF group, and, surprisingly, this advantage was greater than over the HF group. In other words, the results suggested that the acquisition of a second dialect did not enhance the abilities of listeners to perceive differences between phonemic contrasts in that D2. If anything, the acquisition of the D2 disadvantaged the perceptual abilities of the HF>QF group. This might be because these phonemes have, over time, become less acoustically marked for the HF>QF participants and have, potentially, become integrated into their D1 phonemic categories.
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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 | 0.000 | 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) | 0.004 | 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".