Is it better to be average? High and low performance as predictors of employee victimization.
Bibliographic record
Abstract
Given increased interest in whether targets' behaviors at work are related to their victimization, we investigated employees' job performance level as a precipitating factor for being victimized by peers in one's work group. Drawing on rational choice theory and the victim precipitation model, we argue that perpetrators take into consideration the risks of aggressing against particular targets, such that high performers tend to experience covert forms of victimization from peers, whereas low performers tend to experience overt forms of victimization. We further contend that the motivation to punish performance deviants will be higher when performance differentials are salient, such that the effects of job performance on covert and overt victimization will be exacerbated by group performance polarization, yet mitigated when the target has high equity sensitivity (benevolence). Finally, we investigate whether victimization is associated with future performance impairments. Results from data collected at 3 time points from 576 individuals in 62 work groups largely support the proposed model. The findings suggest that job performance is a precipitating factor to covert victimization for high performers and overt victimization for low performers in the workplace with implications for subsequent 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".