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Suspicious Minds: How Perceptions of Victim Signaling Shape Workplace Relationships

2025· article· en· W4416005050 on OpenAlexaff
Logan M. Steele, Hsuan‐Che Huang, Daniel P. Skarlicki, Karl Aquino

Bibliographic record

VenueAcademy of Management Proceedings · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPerceptionAdversarial systemTest (biology)Social perceptionCognitionWork (physics)Social cognitive theory

Abstract

fetched live from OpenAlex

Organizations have recently experienced an increase in employees openly discussing personal experiences of victimization, presenting both opportunities and challenges for workplace relationships. The present research introduces the concept of perceptions of workplace victim signaling (WVS) — defined as an employee’s subjective appraisal of the frequency with which other employees at work publicly communicate their disadvantages, suffering, oppression, or personal limitations. Drawing on paranoid cognition theory (Kramer, 2001), we predict that perceptions of WVS can trigger paranoid cognitions, leading to their expectations of adversarial conflict with coworkers from social groups different from their own (i.e., race, gender, and political orientation). We further propose that these expectations can result in increased withdrawal behavior and a lower tendency to support coworkers. We test our predictions across four studies (N = 1240)—one cross-sectional study, two time-lagged studies, and a preregistered experiment. The results provide support for our theoretical model, offering novel insights into some of the inadvertent consequences employees experience when they perceive frequent expressions of victimization by others at work.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.628
Threshold uncertainty score0.562

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.028
GPT teacher head0.307
Teacher spread0.279 · 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 designQualitative
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

Citations0
Published2025
Admission routes1
Has abstractyes

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