Can two wrongs make a right? The buffering effect of retaliation on subordinate well-being following abusive supervision.
Bibliographic record
Abstract
Subordinates who are abused by a supervisor tend to experience violated perceptions of interpersonal justice and deteriorated well-being. One way in which they may seek to cope with these consequences is by engaging in retaliatory behaviors intended to "get back" at their supervisor and even the score. Based on research suggesting that acts of retaliation can restore perceptions of justice, we propose a model whereby retaliation alleviates the effect of abusive supervision on subordinate well-being by restoring subordinates' interpersonal justice perceptions. In two studies, using multiwave (Study 1) and time-lagged (Study 2) designs, we found general support for our predictions, even when controlling for the alternative mechanism of victim identity and subordinates' baseline well-being. These results suggest that retaliation reduces the harmful consequences of supervisory abuse on well-being not only in the short term but also in the long run. Theoretical and practical implications surrounding this increased understanding of the effectiveness of retaliation as a strategy for coping with the effects of abusive supervision over time are discussed. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".