COUNTERPOINT PIECE: THE CASE FOR VARIETY IN CORRECTIVE FEEDBACK RESEARCH
Bibliographic record
Abstract
Goo and Mackey (this issue) outline several apparent design flaws in studies that have compared the impact of different types of corrective feedback (CF). Furthermore, they argue that SLA researchers should stop comparing recasts to other types of CF because they are inherently different kinds of phenomena. Our response to their article addresses (a) the claim that the recast-learning relationship has been “settled,” (b) the misleading representation of our views on uptake, (c) the characterization of the CF comparison studies as being weak and invalid, and (d) Goo and Mackey’s recommendations concerning the most appropriate approach to investigating the effect of feedback on second language learning.
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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 | 0.138 | 0.342 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.009 | 0.061 |
| Scholarly communication | 0.018 | 0.041 |
| Open science | 0.011 | 0.016 |
| Research integrity | 0.026 | 0.037 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".