Replications Further Examining Workplace Incivility Perceptions based on Personality Characteristics
Bibliographic record
Abstract
This paper aimed to replicate the findings from Sliter, Withrow, and Jex (2015) examining the influence of personality characteristics on how individuals perceive uncivil behaviors in the workplace. The original study found that trait anger, and unexpectedly positive affect, were the strongest predictors or perceived workplace incivility. In addition, it failed to support the hypothesized relationships between perceived incivility and agreeableness, emotional stability and negative affect. To assess success of replication, we used four different criteria (prediction intervals, original study confidence interval, replication confidence intervals, and significance testing) in an independent literal replication (student sample) and an independent constructive replication with a sample of employed participants. In both replications, positive affect and trait anger were the strongest predictors of perceived workplace and replicated across the four success criteria. The constructive replication found different effect sizes of negative affect, agreeableness, and emotional stability, providing some support for the original hypotheses that were not supported in the original study. Finally, the findings related to openness, conscientiousness and extraversion were inconsistent across studies. Taken together these replications suggest the need to continue exploring the role of personality traits in incivility perceptions with constructive replications that provide methodological improvements beyond improving the sampling.
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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 | 0.051 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".