Centering the Relational Context of Moral Transgressions in Morality Research
Bibliographic record
Abstract
Engaging in wrongdoing at work is extremely costly to organizations and employees. Past work suggests that wrongdoing must be condemned in order to mitigate the costs and deter future transgressors. However, much of this past work has ignored the relational context, stripping away established relationships between people involved in the transgression (e.g., between witness and transgressor), and specific attributes of those involved (e.g., demographics). This presents a problem for our understanding of morality, as wrongdoing at work occurs within relational contexts embedded in organizations. The present symposium seeks to broaden our understanding of morality at work by focusing on the relational contexts in which moral transgressions occur. Specifically, we demonstrate how the relationships to a wrongdoer shapes observers’ moral judgments and decisions to report, how the status and behavior of people who condemn transgressions impacts third-party judgments, and how potential transgressors navigate a decision to transgress based on the context. Witnesses of Wrongdoing Overestimate Transgressors’ Silence Expectations at Work Author: Zachariah Berry; Cornell U. Author: Brian J. Lucas; Cornell U. Morality as Relational Toolkit: How Social Closeness Shapes Moral Thinking Author: Daniel Alexander Yudkin; The Wharton School, U. of Pennsylvania Author: Geoff Goodwin; U. of Pennsylvania Author: Sudeep Bhatia; U. of Pennsylvania Minority Employees Face Retaliation for Protecting Peers from Abusive Behaviors Author: Logan Joseph Balfantz; U. of Notre Dame, Mendoza College of Business Author: Michelle Cho; Doctoral Student at Kenan-Flagler Business School, UNC at Chapel Hill Author: Timothy Kundro; U. of North Carolina, Chapel Hill Punishing Without Looking for Reputational Gain Author: Jillian Jordan; Northwestern U. Author: Nour Kteily; Northwestern Kellogg School of Management Look at Me Now! Identifying the Role that Evidence Plays in Dishonest Reporting Author: Samuel Skowronek; UCLA Anderson School of Management
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.033 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".