Psychometric Properties of the German Version of the Workplace Incivility Scale and the Instigated Workplace Incivility Scale
Bibliographic record
Abstract
Abstract. The quality of workplace social environments has been widely recognized as having an important role in employees’ experience of their workplace, which is confirmed by recent research. The greater frequency of incivility, in contrast to the more intense forms of negative workplace interactions, expands opportunities for understanding the social dynamics of workplaces. Important aspects of this research are potential cultural variations in workplace social behavior. Valid translations of measures for the core constructs are part of the essential infrastructure to support such research endeavors. To assess uncivil behavior at the workplace, we prepared a German translation of the Workplace Incivility Scale and the Instigated Workplace Incivility Scale. Our analysis of the responses from 2,168 Austrian workers indicated that the translation of both scales into German was successful, and that the concept of incivility can indeed be transferred to the German-speaking population. The factor solution was comparable to the original version of the scales. Criterion validity coefficients lay in a similar range as the coefficients found in previous studies with Canadian samples. The availability of the scales should stimulate research on incivility among the German-speaking population and can help in organization-diagnostic processes.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".