Episodic envy and counterproductive work behaviors: Is more justice always good?
Bibliographic record
Abstract
Summary The authors examined how perceived event‐specific procedural and distributive justice about own and envied others' outcomes interacts with episodic envy to predict counterproductive work behaviors. Our results were consistent with the attribution model of justice, finding that episodic envy significantly predicted counterproductive work behaviors aimed at envied others in the workplace and that this relationship was more pronounced when perceptions of procedural, but not distributive, justice about own or envied others' outcomes were high rather than low. We tested a moderated‐mediation model in which self‐attributions for the outcome mediated the effect of episodic envy on counterproductive work behaviors and that the effect of envy was stronger when perceptions of own or others' procedural justice were high rather than low. This research contributes to the literature on envy processes in the workplace and is the first to use a specific emotion, envy, as a proxy for a negative outcome in a demonstration of the attribution model of justice. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".