Moral Heroism: What Makes Employees Stand up to, Report, or Stop Unethical Conduct?
Bibliographic record
Abstract
Given the prevalence of unethical behavior in organizations, it is important to determine potential antecedents of morally heroic behaviors. This symposium is devoted to continued exploration of answers to this simple yet not completely answered question: what makes an employee more versus less likely to become a moral hero? The proposed symposium aims to approach the question from two aspects. First, the symposium is intended to expand our current understanding about the predictors of whistle-blowing, the most widely studied but not completely understood type of morally heroic behavior, by exploring how intra-team dynamics (e.g., intra-team ostracism, intro-team helping) could influence whistle- blowing intention and behavior. Second, this symposium aims to motivate research questions surrounding other types of morally heroic behaviors by exploring the predictors of other types of morally heroic behaviors such as moral objection and ethical advocacy. A Social Exchange-Based Model of Ostracism and Whistle-Blowing in Teams. Presenter: Trevor Spoelma; U. of New Mexico Presenter: Nitya Chawla; U. of Arizona Presenter: Aleksander P.J. Ellis; U. of Arizona Presenter: Jeeyoon Park; U. of Arizona Examining the Effects of Helping on Whistle-Blowing Behavior in Organizations. Presenter: Feng Qiu; U. of Oregon Presenter: Ke Michael Mai; National U. of Singapore Presenter: Aleksander P.J. Ellis; U. of Arizona When Do Employees Speak Up Against Unethical Conduct? Team Stage and Moral Objection. Presenter: Kenneth Tai; Singapore Management U. Presenter: Maryam Kouchaki; Northwestern Kellogg School of Management Winning an Ally to Advocate for Ethics in a Business Group. Presenter: Anjier Chen; Pennsylvania State U. Presenter: Linda K Trevino; Pennsylvania State U. Presenter: Carolyn Thi Dang; Pennsylvania State 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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".