Third-Party Perceptions of Victims in the Workplace: New Complexities and Opportunities
Bibliographic record
Abstract
Third parties, or people who learn about or observe others’ mistreatment at work without being directly involved (Skarlicki & Kulik, 2005; Treviño, 1992), play a crucial role in victims’ future workplace outcomes following the initial mistreatment (Dodson et al., 2023). However, the intricacies of the relationship between victims and third parties are under-addressed in the current literature, and we believe scholars have merely scratched the surface of understanding how third-party responses affect victims of workplace misconduct or mistreatment. In this symposium, we present four papers that share novel findings related to how third parties respond in the aftermath of workplace mistreatment and highlight opportunities to continue to expand our understanding of complex interpersonal processes that occur in organizations between third parties, victims, perpetrators, and other organizational stakeholders following mistreatment. Third Parties’ Moral and Social Responses to Workplace Sexual Harassment Victims Author: Rachael Goodwin; Syracuse U. Whitman School of Management Author: Samantha Dodson; Haskayne School of Business, U. of Calgary Author: Jesse Graham; U. of Utah, David Eccles School of Business Author: Morteza Dehghani; U. of Southern California Author: Kristina Diekmann; U. of Utah The Dark Side of Forgiveness: Unintended Consequences of Third-Party Forgiveness on Victims Author: Sarah Jensen; U. of Utah, David Eccles School of Business Author: Xiaoyu Yin; OB Author: Kylie Rochford; U. of Utah, David Eccles School of Business Author: Kristina Diekmann; U. of Utah Taking on EDI: The Ethics of Managers as Modern-day Robin Hoods Author: Samantha Dodson; Haskayne School of Business, U. of Calgary Author: Daniel Skarlicki; U. of British Columbia Moral Disengagement as an Explanation for Failure to Hold Hararassers Accountable Author: Ho Kwan Cheung; U. of Calgary Author: Caren Goldberg; U. de Sevilla Author: Lynn Bowes-Sperry; California State U., East Bay
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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 | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.025 |
| Scholarly communication | 0.016 | 0.031 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".