Bibliographic record
Abstract
This article seeks to illuminate the degree of position-based variation observed in the acquisition of new segments in a second language and to explain such variability as the consequence of phonetic constraints; this approach contrasts with much previous research that has used typological markedness to the same end. Specifically, it is proposed that learners will have the least difficulty acquiring sounds that involve novel combinations of voicing and manner in positions that favor the phonetic implementation of these sounds. Moreover, on the assumption that not all parameters can be mastered simultaneously, it is predicted that learners will first acquire aspects of a segment's articulation that are perceptually salient and articulatorily easier. The data come from a study of the acquisition of French by 20 intermediate- and advanced-proficiency English-speaking learners of French. Acoustic analysis of the data reveals asymmetries that favor accuracy with manner in onsets versus more targetlike realization of voicing in codas, in which devoicing exists in the input. Beyond demonstrating the role of phonetic principles in determining position-based variation, the findings contribute to our understanding of the acquisition of new consonantal contrasts by providing empirical evidence from a non-Germanic language to bear on this line of inquiry.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".