If you build a remedial voice mechanism, will they come? Determinants of voicing interpersonal mistreatment at work
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".