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Record W2126987873 · doi:10.1177/0018726709348937

If you build a remedial voice mechanism, will they come? Determinants of voicing interpersonal mistreatment at work

2010· article· en· W2126987873 on OpenAlex
Karen Harlos

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHuman Relations · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsVoicePsychologyRemedial educationEmployee voiceSituational ethicsInterpersonal communicationSocial psychologyPerspective (graphical)SupervisorPower (physics)Multilevel modelWork (physics)Predictive power

Abstract

fetched live from OpenAlex

This study examined person-centered (gender, work self-esteem) and situational (hierarchical power relations, mistreatment severity, intentionality) variables that determine employee voice to remedy interpersonal mistreatment. Data were collected from graduate business students who responded to a scenario describing exposure to mistreatment by a work colleague. Results suggested that gender, work self-esteem, and relative hierarchical power were most predictive of remedial voice to an internal mediator. Power relations played an important moderating role such that lower power positions seemed to inhibit voice. That is, women would be more likely than men to voice but only when a co-worker (versus supervisor) was the offender. Individuals with low work self-esteem would be less likely to voice than individuals with high work self-esteem when mistreated by a supervisor (versus co-worker). The results support using a social-psychological perspective for identifying determinants of remedial voicing (or hesitation to voice) related to persons, situations, and their interactions.

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.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.306
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.306
Teacher spread0.286 · 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