Incivility and gender: considering instigator/target gender interactions
Bibliographic record
Abstract
In this study, I examine gender dyads, especially considering gender minorities, and how uncivil behaviour would effect negative and positive affect, interactional justice, affective commitment, and turnover intentions. In particular, I use the Dysempowerment Model (Kane & Montgomery, 1998) to explain how incivility leads to these negative outcomes. In addition, selective incivility (Cortina, 2008) is incorporated to illustrate how gender of the target may result in higher rates of polluters, thus different (more significant) outcomes for female and gender minority targets. Finally, I integrate gender status literature to hypothesize gender effects based on the manager’s (instigator) gender. A 2 (incivility vs. control) x 3 (manager gender: male, female, transgender) x 3 (participant/target gender: male, female, transgender) vignette-based pseudo-experiment was conducted to test the hypotheses. Data collection was done online, and participants were recruited through online means (Amazon Mechanical Turk, Social Media recruitment, and Prolific). The main effects for incivility manipulation were all significant, but the gender x incivility interaction effects were mainly nonsignificant. The significant results support that relatively common, but rude, behaviours can cause tangible changes in negative affect, interactional justice, job commitment, affective commitment, and positive affect. Some unexpected interactions were found with respect to intentions to quit. Implications for future research and practitioners are discussed.
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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 | 0.002 | 0.006 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".