Bibliographic record
Abstract
Purpose The purpose of this paper is to bring definitional clarity to the term “virtue” as pertinent to the behavioural sciences literatures on leadership; to identify a short and consolidated list of cardinal virtues commonly associated with leadership effectiveness; to provide a model relating leader virtues to leader outcomes (i.e. ethics, happiness, life satisfaction, and effectiveness); and to propose a program of research. Design/methodology/approach The authors systematically and comprehensively review Aristotelian and Confucian literatures on virtue ethics, and the literatures on seven leadership styles – i.e. moral, ethical, spiritual, servant, transformational, charismatic, and visionary leadership. Findings Six virtues, including four considered cardinal by Aristotle (courage, temperance, justice and prudence), and two considered cardinal by Confucius (humanity, and truthfulness), were common to all seven leadership styles. Research limitations/implications Researchers should aim to develop and validate a measure of virtuous leadership based on the six cardinal virtues presented here and also to test both the proposed measurement and structural models. Practical implications The authors' recommended program of research will ideally inform development and design of selection and training programs for enhancing virtuous leadership. Originality/value The authors provide definitional clarity to the term “virtue” – one that is well grounded in the moral philosophy and virtue ethics literatures; consolidate vast and varied literatures on seven different widely subscribed leadership styles and identify six cardinal virtues most likely to positively impact leadership effectiveness; present an organizing framework, structural model, and research agenda to catalyze research on virtuous leadership.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".