Bibliographic record
Abstract
Affect has been one of the most neglected areas in L2 research with the possible exception of language anxiety. This overall lack of scholarly attention to affect appears to be even more evident in corrective feedback (CF) research. This chapter discusses this relatively under-explored area, describing empirical research conducted so far in relation to the role of affective variables in CF-driven L2 learning. Given the scarcity of relevant research and a space limit, the chapter focuses mainly on language anxiety, learner beliefs/attitudes, emotions, and other related issues (e.g., motivation, self-efficacy). The brief overview of research illustrated in this chapter suggests that affect mediates L2 learning processes involving CF, and that learners’ affective states are often influenced by teacher feedback. Findings also indicate that L2 learners experience changes in affective domains, which in turn lead to varying degrees of intra-individual variability in their perceptions of CF. Nevertheless, the current state of affairs does not offer more than a potential link between CF and affective variables, and, of course, is inconclusive in terms of the extent to which these seemingly important affective variables influence the way CF contributes to L2 learning process and overall 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".