Bibliographic record
Abstract
This study investigated the differential effects of prompts and recasts, in the context of dyadic interaction, on the acquisition of grammatical gender by adult second language learners of French. Participants were 25 undergraduate students enrolled in an intermediate‐level French course at an English‐speaking university. All students were exposed in class to a 3‐hr form‐focused instructional treatment distributed over 2 weeks and were then randomly placed in either the recast or prompt group. On two occasions outside of class, individual students participated in three different oral tasks during dyadic interaction with a native or near‐native speaker of French who, following learner errors in grammatical gender, provided feedback in the form of either prompts or recasts. Pretests and immediate and delayed posttests included two oral production tasks and a computerized reaction‐time binary‐choice test. Results of repeated‐measures ANOVA showed that both groups significantly improved accuracy and reaction‐time scores over time, irrespective of feedback type. We conclude that learners receiving recasts benefited from the repeated exposure to positive exemplars as well as from opportunities to infer negative evidence, whereas learners receiving prompts benefited from the repeated exposure to negative evidence as well as from opportunities to produce modified output.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".