Moral courage, injury, and leadership in military contexts: lessons from a thematic analysis of conversations among international experts and students
Bibliographic record
Abstract
INTRODUCTION: Recent global events have underscored the importance of moral leadership and courage. A series of moderated conversations about moral leadership and dilemmas during times of conflict and crisis were facilitated in 2021 with Lieutenant-General (ret'd) The Honourable Romeo Dallaire, military and global affairs experts and international scholars from North America, Europe, Australia and the global south, together with students from the Netherlands and Canada. OBJECTIVE: To explore topics of moral leadership, courage and dilemmas during adversity. METHODS: = 3). Sessions were recorded, transcribed and thematically analysed. RESULTS: Thematic analysis revealed three critical themes: (1) enhancing awareness of moral leadership, (2) moving towards a new vision of moral leadership, and (3) developing training in moral leadership. CONCLUSION: These results highlight key insights that may guide current and future leaders. In response to societal diversity and global complexities, traditional leadership and organizational practices may need to be reconsidered. In addition to essential leadership skills, emerging leaders need to be supported to be competent, engaged moral leaders. They may also benefit from positive role-modelling and moral leadership training during basic through advanced leadership and pre-deployment training.
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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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".