Bibliographic record
Abstract
Abstract This essay argues that the ethical standards for leaders should not be different from those for everyone else. Leaders are carved out of morally fallible humans who are put in positions where they are expected to fail less than most people. The author believes that understanding the moral challenges distinctive to people in leadership positions is fundamental to understanding the very nature of leadership. This essay discusses a few of these ethical challenges including self‐interest, power, discipline, and trust. In the end, the author suggests that we may gain more insights into leadership development by studying ethical failures of leaders than by studying their successes. Résumé Cet article soutient la thèse que les normes de conduite des chefs (d'entreprise) ou dirigeants devraient être sen‐siblement les mêmes que ceux des effectifs de tous rangs. Les chefs sont faits d'humains peccables que l'on a placés dans des situations où l'on emit qu'ils réussiront mieux que le commun des mortels. L'auteur emit qu'une meilleure compréhension des difficultés morales qu'af‐frontent les chefs en particulier est essentielle pour une compréhension de la nature même du travail de direction. L'article passe en revue quelques‐unes de ces difficultés, dont l'avantage personnel, le pouvoir, la discipline, et la confiance. En conclusion, l'auteur suggère que l‘étude des manquements de conduite, plutôt que des réussites, tend à nous renseigner davantage sur le déve‐loppement des capacités de “leadership.”
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".