New Developments in Unethical Leadership: What it is, its Antecedents and Consequences
Bibliographic record
Abstract
Significant efforts have been devoted to understanding negative types of leadership, including unethical supervisory behaviors. Despite these efforts, much of the research in this area is focused on harmful interpersonal behaviors (e.g., abusive supervision). Yet, unethical leadership entails much more, such as breaking or contravening rules and employing unethical means to influence employees. This symposium aims to present new insights and perspectives from recent empirical research that advances our understanding of unethical leadership and how and why it occurs. Seeing pariahs or prospects? Why leaders behave unethically (or not) toward poor performers. Author: John Lynch; U. of Illinois at Chicago Author: Marie S. Mitchell; U. of North Carolina, Chapel Hill Author: Shubha Sharma; The U. of Tulsa Uneth savior: A dyadic investigation of leaders’ pro-follower unethical behavior toward underdogs. Author: Grace Ching Chi Ho; Arizona State U. Author: Devin Ronald Flake; W. P. Carey School of Business, Arizona State U. Author: David Welsh; Arizona State U. Easily led astray: Subtle changes in how leaders motivate subordinates can increase morally compromi Author: Celia Moore; Imperial College Business School Author: Yaoxi Shi; Imperial College London Intervening the trajectories of leader (un)ethicality over time: A within-person field experiment. Author: Wei Wang; U. of Manitoba Author: Michelle K. Duffy; U. of Minnesota Unethical Leadership: Measure development and validation. Author: Gabriela Rivera; Penn State Smeal College of Business Author: Linda K Trevino; Pennsylvania State U. Author: Marie S. Mitchell; U. of North Carolina, Chapel Hill Author: Anjier Chen; National U. of Singapore (NUS)
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".