The exaggerated benefits of failure.
Bibliographic record
Abstract
Commencement speakers, business leaders, and the popular press tell us that failure has at least one benefit: It fuels success. Does it? Across 11 studies, including a field study of medical professionals, predictors overestimated the rate at which people course correct following failure (Studies 1-4). Predictors overestimated the likelihood that professionals who fail a professional exam (e.g., the bar exam, the medical boards) pass a retest (Studies 1a, 1b, and 2a), the likelihood that patients improve their health after a crisis (e.g., heart attack, drug overdose; Studies 2b and 6), and the probability, more generally, of learning from one's mistakes (Studies 3-5). This effect was specific to overestimating success following failure (Study 4) and erasing mention of an initial failure that had actually occurred corrected the problem (Studies 2a and 2b). The success overestimate was due, at least in part, to the belief that people attend to failure more than they do (Studies 5 and 6). Correcting this overestimate had policy implications. Citizens apprised of the sobering true rate of postfailure success increased their support for rehabilitative initiatives aimed at helping struggling populations (e.g., people with addiction, ex-convicts) learn from past mistakes (Studies 7a-7c). (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.000 | 0.002 |
| 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) | 0.000 | 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".