Bringing the Unattended Center Stage: Exploring Fairness, Ambiguity, and the Path to Redemption
Bibliographic record
Abstract
Gathering an international panel of scholars from Belgium, Canada, the Netherlands, and the United States, our symposium, “Bringing the Unattended Center Stage: Exploring Fairness, Ambiguity, and the Path to Redemption” navigates the multifaceted challenges faced by organizations today. More precisely, this symposium sheds light on unattended justice topics using rigorous scientific inquiry to highlight the justice-related challenges faced by organizations and their members. Combining various methodological approaches, each paper spotlights underexplored justice topics, including (a) how morally questionable (in)actions are associated with different levels of ambiguity, which may elicit distinct processes and implications, (b) individuals’ experiences and responses to allegations of harm in situations marked by ambiguity and complexity, complicating the assessment of culpability, (c) fairness concerns and legitimacy issues arising from need-based distributions across on-site, remote, and hybrid work arrangements, (d) how employees evaluate differentiated vs. individualized HR practices, and (e) whether and when workplace offenders are redeemable, showcasing how managers and employees approach the concept of redemption, change, and restorative justice. Dr. David Patient, esteemed for his fundamental contributions to the management discipline broadly and justice events specifically, will conclude the symposium with an engaging, interactive discussion that highlights key insights and future research directions. This symposium offers actionable insights into addressing fairness concerns in diverse work modes, navigating morally ambiguous actions, and managing complex harm allegations. Together, the symposium equips practitioners with tools to foster fairness, enhance ethical decision-making, and integrate restorative justice into organizational practices. “Do No Harm” Versus “Do Virtue”: Differentiating Sins of Commission and Omission Author: Christine Chi Hye Hwang; University of Guelph Author: Laurie Barclay; University of Guelph Author: Robert Bies; Georgetown University What We Don’t Know About The Consequences of Unverifiable Allegations Author: Maja Graso; University of Groningen Author: Lieven Brebels; KU Leuven Author: Jeroen Camps; Author: Thomas M. Tripp; Washington State University Equality, Equity, and Need: Fairness Perception Across Remote, On-Site and Hybrid Work Author: Ashkan Rostami; Concordia University Author: Tracy Hecht; Concordia University Author: Ariane Ollier-Malaterre; Université du Québec à Montréal (UQAM) The Interaction of Standardized and Differentiated Human Resource Practices on Fairness Perceptions Author: Sylvie Guerrero; UQAM Author: Victor Y. Haines; University of Montreal Author: David Leonard Patient; Vlerick Business School Author: Alain Marchand; The Journey to Redemption: How & For Whom Remorse, Rehabilitation & Restoration Influence Judgments Author: Eunjeong Shin; Berry College Author: Thomas M. Tripp; Washington State University Author: Karl Aquino; The University of British Columbia Author: Robert Bies; Georgetown University
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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 | 0.048 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.027 | 0.067 |
| Scholarly communication | 0.027 | 0.025 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.006 | 0.017 |
| 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".