The Management of Identity-Based Conflicts: New Directions in Justice Research
Bibliographic record
Abstract
This symposium explores a recently intensifying phenomenon of how actors manage their public identities in the #MeToo era – an era that highlights the tensions among those seeking distributive justice, due process, accountability, and endorsing psychological safety. We explore this phenomena through the lens of organizational justice, thereby broadening its application and relevance by raising new research questions and domains of study. In particular, we explore how one’s social identity shapes the aftermath of organizational conflicts, including how harshly (and why) actors’ actions may be judged and whether actors may (or may not) be forgiven, redeemed, and reintegrated. Three papers speak to these topics. Most of the symposium will be devoted to interactive discussions of each paper, then interactive discussion of the broad themes, questions, and challenges. Forgiveness in the Workplace: An Identity-Based Perspective Presenter: Michael E. Palanski; Rochester Institute of Technology Presenter: Laurie Barclay; Wilfrid Laurier U. Presenter: Nicholas Difonzo; Rochester Institute of Technology The Effect of Mindsets and Ex-Offenders’ Redemptive Narratives on Managers’ Willingness to Hire Presenter: Eunjeong Shin; Washington State U. Easier Lie the Heads: Evidence of a Gender Vilification Gap in Appraisals of Employee Misbehavior Presenter: Chuck Howard; U. of British Columbia Presenter: Ramona Bobocel; U. of Waterloo Presenter: Samir Nurmohamed; The Wharton School, U. of Pennsylvania Presenter: Karl Aquino; U. of British Columbia Presenter: Maja Graso; U. of Otago
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".