Constitution of form-orientation: Contributions of context and explicit knowledge to learning from recasts
Bibliographic record
Abstract
This article investigates a language learner’s cognitive ability (i.e., form-orientation) in detecting the corrective nature of non-salient feedback, by reviewing the operationalizations and reported effectiveness of recasts in recent SLA literature. Two directionalities which seem to have resulted in the divergent findings of recast effectiveness will be discussed: (a) operationalized (or intact) explicitness or implicitness of recasts, which designate the level of noticeability (i.e., feedback factors) and (b) contextual variables, including where a study was conducted and participants’ learning background, which ultimately influence the effectiveness of recasts on language development (i.e., learner factors). To expose learner factors accordingly, learning contexts and learner’s explicit knowledge will be discussed as possible variables in forming cognitive orientation. By taking an interdisciplinary approach to explore the constitution of the orientation, namely, SLA, psycholinguistic, and sociocultural approaches, this article concludes that contexts and explicit knowledge interdependently create the cognitive ability that enhances the noticing of implicit recasts, which then arguably determines subsequent language 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 | 0.003 | 0.026 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".