Ethical Leadership: The Role of Ethical Competencies in Doctoral Supervision Context in Canada
Bibliographic record
Abstract
Due to the influence factor in leader-follower relationships, leadership is an ethical undertaking by nature. In doctoral supervision, ethics are a critical competency, especially since it is an authoritative leadership context based on positional power. Doctoral supervisors have the power to impact their students’ well-being and performance, which means that ethics and ethical leadership could be the most important competency that can make the difference in effective supervision. In this chapter, we examined the nature of ethics and ethical leadership in doctoral supervision based on the supervisors’ and doctoral students’ lived experiences and perspectives. Data analysis of the participant’ responses revealed the importance of key ethical competencies in the context of graduate supervision: commitment, stewardship, honesty, justice/fairness, benevolence, nonmaleficence, respect, and autonomy. The data have shown that these competencies are vital within the doctoral supervision context as they can help maintain students’ well-being and enhance their performance. Findings suggest that attention to ethical practices is key to the development of positive supervisory relationships and the implementation of successful doctoral programs.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".