Attitudinal bias, individual differences, and second language speakers’ interactional performance
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
Abstract This study examined whether an interlocutor’s attitudinal bias affects second language (L2) speakers’ recall of narratives and their responses to corrective feedback (recasts) and whether the role of attitudinal bias depends on individual differences in speakers’ background and personality characteristics. After receiving a positive or negative attitudinal bias orientation, 70 L2 English speakers completed tasks with an interlocutor who provided recasts in response to language errors. Speakers also completed questionnaires targeting individual differences in their motivation and acculturation to the home and target cultures. There were no general effects for positive or negative attitudinal bias on speakers’ recall of personal narratives or responses to feedback. However, under negative bias, motivation scores were associated with speakers’ accurate reformulation of errors. Under positive bias, there was an association between accurate narrative recall and greater psychological adaptation and motivation. Results imply that attitudinal bias plays a subtle role in L2 speakers’ interactional performance.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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 it