Evolution of Self-Repair Behaviour in Narration Among Adult Learners of French as a Second Language
Bibliographic record
Abstract
Self-repairs, or revisions of speech that speakers themselves initiate and complete (Salonen & Laakso, 2009), have long been associated with second language (L2) development (e.g., Kormos, 2000a). To our knowledge, however, no research has looked at the evolution of self-repair correctness patterns, that is, the correctness of elements targeted for repair and the correctness of the repair outcomes. Consequently, the present study sought to investigate changes in the self-repair behaviour of English-speaking L2 learners of French over the course of a 5-week period and to verify whether any changes occurred over time. Speech samples of the L2 were collected from 50 adult participants through an elicited narration task at the beginning (Time 1) and the end (Time 2) of a 5-week immersion program. Overall, the results showed that there were qualitative and quantitative changes in self-repairs types, and that correctness of the element being repaired increased significantly over time.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".