“Incivility, social undermining, bullying…oh my!”: A call to reconcile constructs within workplace aggression research
Bibliographic record
Abstract
Abstract Research in the field of workplace aggression has rapidly developed in the last two decades, and with this growth has come an abundance of overlapping constructs that fall under the broad rubric of workplace aggression. While researchers have conceptually distinguished these constructs, it is unclear whether this proliferation of constructs is adding appreciably to our knowledge, or whether it is constraining the questions we ask. In this paper, I consider five example constructs (i.e., abusive supervision, bullying, incivility, social undermining, and interpersonal conflict) and argue that the manner in which we have differentiated these (and other) aggression constructs does not add appreciably to our knowledge of workplace aggression. I then provide supplementary meta‐analytic evidence to show that there is not a predictable pattern of outcomes from these constructs, and propose a restructuring of the manner in which we conceptualize workplace aggression. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.108 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.003 | 0.027 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".