Making Excuses? Don’t Feel Bad About It! On Instrumentality of External Attributions
Bibliographic record
Abstract
Seminal theories of attribution posit that negative feedback can be a particularly valuable diagnostic tool that illuminates areas of improvement (Dweck & Leggett, 1988). However, prior research has shown that only those performance discrepancies that are attributed to internal and controllable, rather than external and uncontrollable, causes are likely to produce behavior changes in the service of goal attainment (Weiner, 2001). This paper amends the prevalent view of personal responsibility for negative outcomes as imperative for adaptive behavior change. Relying on the regulatory focus theory, the paper posits instrumentality of both internal and external attributions of negative performance feedback under distinct regulatory foci: While internal failure attributions may drive the eager regulatory state in promotion focus, external attributions may help support the vigilant regulatory state necessary for prevention-focused pursuits.
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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".