New Insights on Workplace Mistreatment: Bystander, Target, Perpetrator, and Group Perspectives
Bibliographic record
Abstract
Despite substantial insights from prior research, many critical questions about workplace mistreatment remain unanswered. For instance, while both researchers and practitioners emphasize the importance of bystander intervention, little is known about how engaging in such actions affect the bystanders themselves. Similarly, although the harmful effects of leaders’ aggressive behavior on target employees’ work outcomes are well-documented, it is unclear how employees perceive and respond to aggression when it is expressed through humor—a form of communication typically associated with building rapport and fostering positivity. Additionally, questions remain regarding whether employees may misinterpret well-intentioned and constructive actions, such as providing feedback, as abusive or aggressive. Furthermore, while the adage “power corrupts” is well- established, some studies suggest that power can also enhance one’s sense of responsibility. The conflicting evidence and perspectives highlight a gap in understanding the nuanced relationship between power and engagement in mistreatment behaviors. Finally, although research on workplace aggression has grown significantly, there remains a notable scarcity of research on interventions aimed at reducing it—an area that holds considerable promise for practical applications in managing workplace mistreatment. Accordingly, our understanding of workplace mistreatment is still incomplete, necessitating further investigation to uncover new insights. This symposium presents five papers that explore these questions. It considers workplace mistreatment not only from the traditional perspectives of the bystander, target, and perpetrator but also from the broader group perspective. From Bystander to Upstander: The Ripple Effects of Intervening on Bystanders Themselves Author: Rui Zhong; The Pennsylvania State University Author: Yijue Liang; George Mason University Author: Zhanna Lyubykh; Simon Fraser University Author: Ivana Vranjes; Tilburg University The Implications of Leader Humor on Employee Image Management Author: Shubha Sharma; University of Tulsa Can Priming Hostility Lead to Perceiving Abuse in Supervisor Feedback? Author: Hyewon Ji; Author: Huiwen Lian; Texas A&M University Author: Sijun Kim; Texas A&M University Author: Srikanth Paruchuri; Texas A&M University The Deviant (and Beneficial) Effects of Power Sensitivity Author: Nicolais Chighizola; Air Force Academy Author: Trevor Foulk; University of Florida An Interdependence Theory-Based Network Intervention to Reduce Workplace Ostracism Author: Susan Zhu; University of Kentucky Author: Giuseppe Labianca; University of Massachusetts Amherst Author: Nicolina Leeann Taylor; University of Wyoming Author: Seong Won Yang; University of Mississippi Author: Robert Wilhelm Krause; University of Kentucky Author: Dale Watson; The Pennsylvania State University-Penn State Harrisburg Author: Noelle G Otto; University of Kentucky
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".