Moral Equivalence Through Benevolence, Licensing, and Cleansing
Bibliographic record
Abstract
In the past six years, the relationship between virtue and vice has aroused extensive attention from organizational ethics scholars, and research within this area of inquiry generally supports a view of moral equivalence: that an invisible mental scale plays a role in people’s moral judgements and decisions in the workplace (Klotz & Bolino, 2013). On the one hand, virtue that are prosocial or pro-organizational are argued to license vice such as unethical behavior (Monin & Miller, 2001) and counterproductive work behavior (Klotz & Bolino, 2013). On the other hand, vice is often deemed compensable or justifiable through moral cleansing (i.e., engaging in subsequent good deeds; Tetlock, Kristel, Elson, Green, & Lerner, 2000), literal cleansing (i.e., hygiene; Zhong & Liljenquist, 2006), or having good and prosocial intentions (Umphress & Bingham, 2011). Although the moral equivalence view has received both theoretical and empirical support, our understanding about it is still relatively nascent. At present, we know little about the context and boundary conditions under which moral equivalence plays a role in people’s moral decisions and judgments. This suggests that next generation research on moral equivalence should explore contexts and conditions that trigger people’s mental scale for moral issues, such as to what extent licensing effects may cross work-life boundaries, how licensing affects judgments we make of others, and whether credits may actually be “borrowed” proactively across time horizons. In light of this, our symposium aims to deepen our understanding of moral equivalence by bringing together five papers that explore different contexts, as well as conditions that can color people’s moral decisions and judgements. The goal of the proposed symposium is to explore and motivate “next generation” questions surrounding moral equivalence in the workplace. Vicarious Moral Licensing at Work: When Does Follower Citizenship License Leader Deviance? Presenter: Ghufran Ahmad; Lahore U. of Management Sciences Presenter: Anthony Klotz; Oregon State U. Presenter: Mark C Bolino; U. of Oklahoma Unethical gratitude? Communal Sharing Predicts Gratitude After Others Unethical Behavior Presenter: S Wiley Wakeman; London Business School Moral Licensing Effects of Positive Parenting Presenter: Feng Qiu; U. of Oregon Presenter: David T. Wagner; U. of Oregon Presenter: Lei Huang; Auburn U. Presenter: Keith Norman Leavitt; Oregon State U. An Investigation of the Effects of Hygiene Behaviors on Moral Consequences Presenter: Jenny Hejia Wang; Georgia State U. Presenter: Tianyu He; INSEAD Presenter: Colin West; UCLA Anderson School of Management Presenter: Chen-Bo Zhong; U. of Toronto An Angel Now to be a Demon Later: A Moral Pre-Cleansing Model Presenter: Denton Hatch; U. of Arizona Presenter: Han Jiang; U. of Arizona
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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 | 0.003 | 0.019 |
| 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 | 0.016 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".