Recast frequency and the acquisition of English articles in a computer-mediated context
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study examines the role of recast frequency and its effectiveness in the acquisition of English articles in a computer-mediated context. Sixty-one pre-intermediate university language learners in Turkey were randomly divided into four main groups: high frequency recast (HF), low frequency recast (LF), test control, and task control groups. The learners in the HF and LF recast groups completed five and two tasks, respectively, in a video-conferencing environment and received oral recasts on their incorrect use of English articles. Learners in the test control group only took the pre and posttests, and learners in the task control group completed five tasks without receiving feedback on the target structure. The outcome was measured through online picture description and error correction tasks. Findings showed that in the picture description task, learners in the HF group performed significantly better than those in the LF recast group and the control groups. In the error correction task, the results revealed a short-term advantage for learners in the HF group, which faded away in the delayed posttest. Significant correlations were also found between the recast frequency and learners’ score improvement in the immediate and delayed picture description tasks but not in the error correction tasks. These results suggest that recast quantity may play an important role in improving learners’ accuracy of their oral production.
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.
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 | 0.002 | 0.001 |
| 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 | 0.001 |
| 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 it