Advances in Behavioral Ethics: Moral Consistency, Licensing, Cleansing, and Transgressions
Bibliographic record
Abstract
As the research on moral self-regulation, or the psychological processes and cognitive biases by which individuals regulate their moral behavior over time, has gained popularity in management and applied psychology over the past two decades, limitations to our understanding of this theoretical framework have become more salient. This symposium is comprised of three presentations that attempt to address several different, but related, issues. More specifically, the presenters will discuss research designed to: (a) address gaps or inconsistencies in moral self-regulation research for organizational behavior and organizational ethics, and (b) examine how the broader social context effects individual’s moral judgements and moral self-regulatory processes over time. The symposium includes both narrative review and model building, as well as experimental designs. The presenters will discuss the potential implications of their findings for management scholars and practitioners. IntraIndividual (Un)Ethical Behavior in Management: A Review and Recommendations for Future Research Presenter: Benjamin G. Perkins; U. of Arizona Presenter: Nathan Philip Podsakoff; U. of Arizona Presenter: David Welsh; Arizona State U. Giving-by-Proxy Triggers Subsequent Charitable Behavior Presenter: Samantha Kassirer; Northwestern Kellogg School of Management Presenter: Jillian Jordan; Northwestern U. Presenter: Maryam Kouchaki; Northwestern Kellogg School of Management Worse to Be First? Victim and Transgressor Perspectives of “Viral” Violations Presenter: Julia A. Langdon; London Business School Presenter: Daniel A. Effron; London Business School
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.034 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| 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".