Star light, but why not so bright? Investigating nonstars' attributions on star performance
Bibliographic record
Abstract
While star performers are commonly believed to exhibit disproportionally stronger job performance, recent research demonstrates this is not always the case when stars move to new organizations. In this article, we draw insights from attribution and dual envy theories to develop a conceptual framework that investigates how nonstars make attributions about star status and the subsequent effect on star performance. In particular, we develop a typology of four different attributions that nonstars make about how an individual attained their status as a star. In turn, these star status attributions result in either benign or malicious envy. Nonstars who experience benign envy engage in relational-enhancing behaviors that positively affect star performance, whereas nonstars who experience malicious envy engage in relational-inhibiting behaviors that negatively affect star performance. Our conceptual model sheds light into the under-researched role of nonstars in shaping star performance with an elaboration of the critical cognitive-based processes through which nonstars help and harm star performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".