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A Review of Witnessed Workplace Mistreatment: Boundary Conditions and Relative Importance

2023· review· en· W4385217073 on OpenAlexaff
Zhanna Lyubykh, Rui Zhong, The Ton Vuong, Sandra L. Robinson, Sandy Hershcovis

Bibliographic record

VenueAcademy of Management Proceedings · 2023
Typereview
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsUniversity of CalgarySimon Fraser University
Fundersnot available
KeywordsBoundary (topology)PsychologyMathematicsMathematical analysis

Abstract

fetched live from OpenAlex

This study aims to understand the role of observers in the dynamic of workplace mistreatment. Through a meta-analytic review, we examine the conditions under which observers respond more or less constructively to mistreatment incidents and the relative importance of witnessed versus experienced mistreatment in terms of overall impact on employees. The results show that observers tend to have negative responses towards perpetrators, though responses towards targets are mixed. Furthermore, these observer responses are contingent on the study design, mistreatment source, and mistreatment ambiguity. The study also finds that experienced mistreatment has a stronger impact on employees than witnessed mistreatment, although witnessed mistreatment still has a sizable impact. The implications of these findings and future directions for research on witnessed mistreatment are discussed.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.049
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.014
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.049
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0140.012
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.068
GPT teacher head0.392
Teacher spread0.324 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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
Published2023
Admission routes1
Has abstractyes

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