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Record W4406808156 · doi:10.1002/job.2854

Perceiving the Inevitable: Understanding Observer Reactions to Workplace Mistreatment Through the Lens of System Justification Theory

2025· article· en· W4406808156 on OpenAlexafffund
Zhanna Lyubykh, Laurie J. Barclay, Nick Turner, M. Sandy Hershcovis

Bibliographic record

VenueJournal of Organizational Behavior · 2025
Typearticle
Languageen
FieldDecision Sciences
TopicEthics in Business and Education
Canadian institutionsUniversity of CalgaryUniversity of GuelphSimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsObserver (physics)PsychologyLens (geology)Social psychologyThrough-the-lens meteringEngineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.835
Threshold uncertainty score0.452

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.253
GPT teacher head0.410
Teacher spread0.156 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2025
Admission routes2
Has abstractyes

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