Assisting failure‐prone individuals to navigate achievement transitions using a cognitive motivation treatment (attributional retraining)
Bibliographic record
Abstract
Abstract Transitions to novel achievement settings are often accompanied by unfamiliar learning conditions and unanticipated failure that undermine how individuals adapt to such situations. For first‐year students, the transition to college is imbued with adverse learning conditions that can result in decreased motivation and academic performance. This study examined the efficacy of a motivation‐enhancing treatment, attributional retraining ( AR ), to assist students who are at risk because of a high‐failure avoidance orientation (tendency to maintain self‐worth by avoiding failure). For high‐ (but not low) failure avoidance students, AR fostered an adaptive psychological mindset (course grade expectations, judgments of course responsibility) and better academic performance (course grade, grade point average). Findings suggest the utility of AR to offset the negative effects of a high‐failure avoidance self‐worth orientation.
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How this classification was reachedexpand
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 | 0.001 | 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) | 0.001 | 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 teacher head, 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".