A DYNAMIC LOOK AT L2 PHONOLOGICAL LEARNING: Seeking Processing Explanations for Implicational Phenomena
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Bibliographic record
Abstract
This study investigates whether second language (L2) phonological learning can be characterized as a gradual and systematically patterned replacement of nonnative segments by native segments in learners' speech, conforming to a two-stage implicational scale. We adopt a dynamic approach to language variation based on Gatbonton's (1975, 1978) gradual diffusion framework. Participants were 40 Quebec Francophones of different English proficiency levels who produced 80 tokens of English in eight phonetic contexts. In Analysis 1, production accuracy data are subjected to implicational scaling, with phonetic contexts ordered solely by a linguistic criterion—sonority hierarchy. In Analysis 2, the production accuracy data are similarly analyzed but with phonetic context ordering determined by psycholinguistic (processing) criteria—cross-language perceptual similarity and corpus-based estimates of lexical frequency. Results support and extend Gatbonton's framework, which indicates that L2 phonological learning progresses gradually, conforming to an implicational scale, and that perceived cross-language similarity and lexical frequency determine its course.This research was made possible through grants to Pavel Trofimovich, Norman Segalowitz, and Elizabeth Gatbonton from the Social Sciences and the Humanities Research Council of Canada (SSHRC) and support from the Centre for the Study of Learning and Performance at Concordia University. The authors gratefully acknowledge the assistance of Melanie Barrière and Randall Halter in all aspects of data collection and analysis. Many thanks are extended to Dawn Cleary, Winnie Grady, Eva Karchava, Nootan Kumar, Magnolia Negrete Cetina, and Alin Zdrite for their help in various stages of this study. The authors wish to thank Tracey Derwing and Murray Munro for sharing their speech elicitation materials. Sarita Kennedy, Randall Halter, and five anonymous SSLA reviewers provided helpful suggestions on earlier drafts of this manuscript.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 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 it