In Extremis: An international Perspective on Military Leadership Training in Extreme Situations
Bibliographic record
Abstract
This comparative study explores how military leadership is developed and executed in extreme situations across diverse national contexts, including Switzerland, South Africa, India, Israel, and Canada. Each country adapts its leadership training and doctrine based on historical experiences, operational demands, and institutional culture. While some militaries, like Canada, emphasize values-based leadership aligned with democratic principles, others, such as India, prioritize cultural ethos and leading by example. Conscription-based systems like Israel’s and Switzerland`s rely heavily on early, experiential leadership, while professional forces like South Africa focus on psychological resilience and adaptability. Training approaches range from academic and doctrinal frameworks to immersive, high-stress simulations. Common across all contexts is the recognition that effective leadership under pressure requires a balance of technical competence, moral integrity, and emotional resilience. The findings underscore the importance of context-specific leadership training models tailored to modern security challenges.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".