The Impact of Status Differentials and Facilitated Feedback on Observer Responses to Abusive Supervision
Bibliographic record
Abstract
When do third parties, individuals who are not the direct target of an act of mistreatment, choose to intervene or abstain from addressing abusive supervision on behalf of a target? Despite progress on understanding the causes and outcomes of abusive supervision, little research examines third-party reactions, and even less devotes attention to contextual factors that shape observer reactions. Given that supervisor-subordinate interactions are situated within and influenced by the broader environment that shapes individuals’ interpersonal relationships and work experiences, this oversight is critical to understanding when observers of abusive supervision choose to intervene on behalf of victims. Drawing from Opotow’s (1995) moral exclusion theory, this study investigates the implications of perceived utility in relation to a victim’s performance and status relative to the supervisor. To narrow the gap between an observer’s intended actions and actual behaviour towards addressing perceived mistreatment, this study introduces the use of facilitated feedback as a protective mechanism against the perceived status differences and risks of retaliation that may deter from expressing supervisory concerns. Through conducting a scenario laboratory experiment among 240 undergraduate students, this study offers practical insights and research implications into the contextual mechanisms that either facilitate or hinder confrontational responses towards abusive supervisors.
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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 | 0.011 | 0.112 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".