Phonetic variability and the variable perception of L2 word stress by French Canadian listeners
Bibliographic record
Abstract
This study investigates development and individual attainment in the perception of word stress by French Canadian second language (L2) learners of English at three proficiency levels (intermediate, low-advanced, high-advanced). It aims to determine whether a perceptual or a processing deficit is responsible for their so-called stress `deafness' (e.g., Dupoux et al., 1997, 2001, 2008). Seventy-five French Canadian L2 learners of English and 31 native English speakers completed an AXB perception task in English where contrast type (stress, segmental) and phonetic variability (with, without) were manipulated, but where processing demands were relatively low. The results indicate that the L2 learners had more difficulty perceiving English stress in the presence than in the absence of phonetic variability. Yet, their perception of stress in the phonetically variable condition was above chance and improved as the number of trials increased. Although the three proficiency groups did not perform significantly differently on the experiment, the L2 learners' self-reported per cent daily use of English was found to be a significant predictor of their successful perception of phonetically variable stress. Given these findings, it is argued that French listeners' reported lack of success in the perception of word stress is unlikely to stem from a perceptual deficit.
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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 | 0.002 |
| 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) | 0.002 | 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".