Ethics at the Fringe: Using Novel Methods & Data to Answer Imperative Questions in Behavioral Ethics
Bibliographic record
Abstract
Behavioral Ethics has come to be viewed as a legitimate sub-field of Organizational Behavior only recently. To date, the field has largely focused on a critical but relatively narrow set of dependent variables (dominantly: lying, cheating, and stealing), and a relatively narrow set of methods (lab experiments and survey data using self- or other-reported unethical behavior). Though this work has been crucial in developing our understanding of the psychological processes that underpin specific types of unethical behavior, the application of these findings may not extend to the diverse and wide-ranging set of unethical practices that we, as a society, need to better understand. In this symposium, we focus on ethics at the fringe – the research questions, unethical behaviors, settings, and methodologies that have remained largely neglected by the field. The papers in this symposium examine extreme, relatively rare unethical behaviors, including workplace violence, mass shootings, political scandals, inappropriate prescribing, and illicit workplace romance. The empirical approaches utilize a variety of methodologies, ranging from archival data analysis to natural field experiments. Workplace Violence: A Schema Perspective Presenter: Katherine Ann DeCelles; U. of Toronto Presenter: Nir Halevy; Stanford U. Business as Usual: Consumer Behavior Following Mass Shootings in America Presenter: Lamar Pierce; Washington U. in St. Louis Presenter: Daniel Snow; Oxford U., Saïd Business School Presenter: Dennis Zhang; Washington U. in St. Louis Crossing the Line or Creating the Line: Media Effects in the 2009 British MP Expense Scandal Presenter: Jonathan Nicholas Bundy; Arizona State U. Social Influence and the Initiation and Cessation of Inappropriate Prescribing Presenter: Shu Zhang; Yale School of Management Forbidden Yet Functional: A Self Categorization Model of Illicit Workplace Romance Presenter: Keith Norman Leavitt; Oregon State U. Presenter: Christopher Barnes; U. of Washington
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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 | 0.325 | 0.511 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.008 | 0.070 |
| Scholarly communication | 0.023 | 0.034 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".