Approaches and methods for the study of the L2 production of French segmentals and prosody
Bibliographic record
Abstract
Abstract This chapter reviews common approaches and methods in studies of the effects of crosslinguistic influence, input, and training/instruction as well as development and individual variability in second language learners’ production of French segmentals and prosody. Discussion focuses first on methodology including speech types, participant characteristics, and frequently used tasks and stimuli. Next, we turn to recurrent practices in the collection, preparation, and analysis of auditory and articulatory data as well as ethical considerations. The major contributions of L2 French pronunciation research are then outlined including the breadth of structures studied, the uniqueness of certain phenomena investigated, innovations in research methodology including corpus-based research, and the unique insights provided into the role of orthography. We conclude with recommendations for future avenues of research such as the parallel study of pronunciation and other linguistic competences, the greater use of corpora, and the integration of research findings within evidence-based pedagogy.
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How this classification was reachedexpand
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 | 0.020 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".