Bibliographic record
Abstract
Despite some progress in instilling ethics into business practice, businesses continue to make decisions that result in incredible harms to people and the environment around the world. Academics, the public, and the media have often singled out business leaders as unethical and responsible for the vast harms that their companies have done. As a result, some have looked to ethical business leadership as one avenue of approach to making businesses act more ethically. This thesis explores two leadership styles. The first is Machiavellian leadership, which has a reputation for being one of the least ethical leadership styles, and the second is transformational leadership, which has a reputation for being one of the most ethical leadership styles. This thesis attempts to align the ethical components of transformational leadership with Machiavellian leadership so that Machiavellian leaders will have reason to behave more ethically. The hope is that some component of transformational leadership theory can inspire Machiavellian leaders to behave more ethically. However, I argue that transformational leadership is not as ethical as it seems, since transformational leadership can result in both ethical and unethical behaviours. Ultimately, I conclude that there is nothing in transformational leadership theory that can inspire Machiavellian leaders to behave more ethically. The frightening implications of this conclusion are discussed.
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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".