Eye gaze and L2 speakers’ responses to recasts: A systematic replication study of McDonough, Crowther, Kielstra and Trofimovich (2015)
Bibliographic record
Abstract
To confirm the role of social factors in mediating cognitive processes, this systematic replication study seeks to extend the generalizability of an exploratory study (McDonough, Crowther, Kielstra & Trofimovich 2015) that reported a positive association between eye gaze and second language (L2) speakers’ responses to recasts. For this replication, L2 English speakers (N = 74) carried out communicative tasks with research assistants who provided recasts in response to non-targetlike forms while both interlocutors’ eye gaze behavior was tracked. Transcripts were analyzed for the occurrence of recasts in response to different error types, recast length, and L2 speaker responses. Eye gaze length for the research assistants (RAs) when producing the recast move and the L2 speaker when responding to the recast were obtained in seconds, and mutual gaze (i.e., simultaneous looking) was included as a binary eye gaze variable. A logistic regression model confirmed the findings of McDonough et al. (2015), with both L2 speaker and mutual eye gaze predictive of targetlike responses; however, the effect of L2 speaker's eye gaze duration was in the opposite direction as compared to the initial study. The implications are discussed in terms of understanding the role of eye gaze in face to face interaction.
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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 | 0.021 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".