Stumbling into Design: Action Experiments in Professional Military Education at Canadian Forces College
Bibliographic record
Abstract
Operations for small militaries are largely about aid to civil power within their home states and what has been termed “contribution warfare” in Canada. The War College is meant to teach the leaders of armed forces (and increasingly, public servants from security related departments) the knowledge necessary to “lead the institution”. In terms of strategy and operations, it is all about linking policy to military operations through the “ends/ways/means” construct of modern strategic theory. In small militaries, however, operational design is generally the purview of the leading coalition partner (typically the US, though sometimes NATO), and the policies adopted by the state may often have relatively little to do with achieving specific military objectives. Despite this fundamental epistemic challenge, well-educated military leaders have never been more important in the charged media environment in which contemporary operations are conducted. So how should senior officers be taught at the highest levels? This article examines the experience of working through this specific pedagogical challenge through the lens of “Design thinking”. It explores the origin and development of the Canadian Forces College’s “Modern Comprehensive Operations and Campaign Design” course taught to colonels and senior public servants on the National Security Programme.
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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 | 0.042 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".