Broadening Our Understanding of ""Doing Justice"" in Organizations
Bibliographic record
Abstract
Many of society’s pressing challenges — conflict, discrimination, well-being — can be linked to injustice within organizations. Effectively addressing workplace injustice requires scholars to broaden our understanding of what it means to “do justice” in organizations. In this symposium, we aim to “broaden our sight” (Academy of Management, 2020) by bringing together a diverse panel of leading scholars who are tackling critical questions related to the obstacles and challenges associated with fostering justice and fairness in the workplace. Using disparate methodologies (e.g., experiments, field studies, interviews) and theoretical frameworks (e.g., appraisal theory, fairness theory, goal prioritization, moral disengagement), our presenters provide insight into (a) the situational factors that can impact whether managers act justly, (b) who is likely to enact justice and the role of emotions in the process of enactment, (c) how managers make sense of having acted unjustly, (d) how perceptions of unfairness can stem from a mismatch between the amount of trust one desires and receives, and (e) the process by which managers are personally blamed and held accountable for unfair situations. The symposium will conclude with an interactive discussion that highlights key themes and directions for future research as well as practical insights into how managers and organizations might promote fairer workplaces by encouraging justice enactment among managers and effectively managing fairness for employees. Are Managers Strategic About Behaving Fairly? Subordinate Uncertainty and Manager Fairness Enactment Presenter: Midori Nishioka; U. of Waterloo Presenter: James W. Beck; U. of Waterloo Presenter: Ramona Bobocel; U. of Waterloo Understanding Interpersonal Justice Enactment by Integrating the ‘Who’ and the ‘Why’ Presenter: Annika Hillebrandt; Ted Rogers School of Management, Ryerson U. Presenter: Maria Francisca Saldanha; UCP - Católica Lisbon School of Business & Economics Presenter: Daniel Brady; Wilfrid Laurier U. Quo Vadis? Three Moral Journeys of Justice Enactment During Organizational Change Presenter: Julia Zwank; EBS Presenter: Marjo-RIitta Diehl; EBS International U. Presenter: Marion Fortin; U. of Toulouse I, Capitole Under, Over, or “Just Right”? The Fairness of (In)congruence Between Trust Wanted and Trust Received Presenter: Michael Baer; Arizona State U. Presenter: Emma Laier Frank; U. of Georgia Presenter: Fadel Khalil Matta; U. of Georgia Presenter: Margaret M. Luciano; Arizona State U. Presenter: Edward McClain Wellman; Arizona State U. Gendered Blame and its Implications for Managers in the Aftermath of Unfair Situations Presenter: Christianne Varty; Wilfrid Laurier U. Presenter: Laurie Barclay; Wilfrid Laurier U. Presenter: Daniel Brady; Wilfrid Laurier U.
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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 | 0.033 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.109 |
| Scholarly communication | 0.028 | 0.035 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 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".