Differences among women in response to workplace incivility
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the consequences of experienced workplace incivility when female employees perceive that they are different from their workgroup. The authors examine how women’s perceptions of demographic dissimilarity from their workgroup moderate the relationships between incivility and psychological wellbeing, and between incivility and turnover intentions. Design/methodology/approach A total of 125 female employees of a post-secondary institution participated in this study. Participants were recruited through an electronic mailing list over the course of one month and completed an online survey. Findings Experienced workplace incivility among females is related to poorer psychological wellbeing and higher turnover intentions. Controlling for actual age and gender dissimilarity at the department level, perceived gender dissimilarity from one’s workgroup moderated the workplace incivility-turnover intentions relationship, whereby the relationship was strengthened at low, but not high levels of experienced incivility. Perceived gender dissimilarity did not moderate the incivility–psychological wellbeing relationship. Perceived age dissimilarity was not a significant moderator. Research limitations/implications The role of perceived dissimilarity and other personal contextual variables should be considered in future work on selective incivility. Perceived dissimilarity can influence some of the negative outcomes associated with incivility, particularly at low levels. Originality/value This research extends the selective incivility literature by incorporating a relational demography perspective to the study of female targets’ experience of workplace incivility. Findings suggest that perceptions of difference may affect the interpretation and outcomes associated with females’ experience of incivility.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".