Maturing metalinguistically : negotiation of form and the refinement of repair
Bibliographic record
Abstract
Research has shown that children attending immersion programs reach a native-like level in comprehension and in reading by the end of elementary level. However, in writing and speaking, they rarely achieve target-like proficiency. Some conditions seem to favor the production of output. This study presents an investigation of children's ability to notice errors in their French second language in immersion program in Montreal. The study was conducted with forty-three (43) children aged 8-9, and aimed to gather information related to the following research questions: Can we train 8 year-old second language learners to: (a) notice their errors; (b) self-correct (given certain prompts); (c) use metalinguistic terminology to identify forms; and (d) negotiate form using language as a conscious tool to improve their L2 oral production? Children were required to participate in two (2) stages: first, video recording of communicative activities whit ungrammatical episodes with provision of corrective feedback were selected; and second, audio recording of children's attempts to negotiate form. The database was collected from these stimulated recall sessions of collaborative discussion. Results show how young learners may benefit from the provision of metalinguistic information, thus facilitating their second language learning development.
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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 | 0.002 | 0.012 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".