Perceiving the Inevitable: Understanding Observer Reactions to Workplace Mistreatment Through the Lens of System Justification Theory
Bibliographic record
Abstract
ABSTRACT System justification theory posits that individuals tend to justify and maintain the status quo. For workplace mistreatment, we argue this tendency can elicit psychological processes in observers that may further disadvantage targets of mistreatment. We propose that organizational climates that are perceived to tolerate mistreatment increase the likelihood that observers perceive specific instances of mistreatment as inevitable. This can activate system justification tendencies in which observers evaluate the mistreatment incident as more legitimate and the target as less legitimate, prompting harmful observer reactions (e.g., minimizing the incident, negatively gossiping about the target). To investigate system justification in observer reactions, we validate a measure of perceived mistreatment inevitability and conduct a multiwave survey to test our hypotheses. Our findings indicate that organizational climates that tolerate mistreatment increase observers' perceptions that specific instances of mistreatment are inevitable, thereby activating processes that prompt observers to justify and maintain the status quo. Theoretical implications include identifying what activates system justification, why observers justify mistreatment, and how these tendencies elicit harmful reactions further disadvantaging targets. Practically, our findings highlight the importance of addressing organizational climates that tolerate mistreatment, avoiding reliance on observers to intervene constructively, and effectively addressing mistreatment to prevent further harm to targets.
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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 | 0.011 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".