Why Improvement Can Trump Consistent Strong Performance: The Role of Effort Perceptions
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 How do evaluators form comparative judgments of improved versus consistently strong performance records with equivalent recent performance? We propose that evaluators judge those with improved records to be more deserving of future opportunities (e.g., a promotion) and that this can be explained – at least in part – by perceptions of effort investments. Specifically, evaluators rely on improvement to judge effort and hence devalue consistent strong performance. Five studies supported these propositions. Evaluators perceived greater effort investment and trait effort in individuals with improved profiles than those with consistent profiles and consequently thought that those with improved profiles were more deserving of future opportunities. We discuss implications of these results across various decision contexts. Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it