Emperor has no clothes: the Canadian perspective for capacity building operations.
Bibliographic record
Abstract
Canada is likely to continue using the Canadian Armed Forces (CAF) to build capabilities in fragile states and stabilize conflict-affected states in Africa, Eastern Europe, and the Middle East in the near term. The conduct of capacity building (CB) operations is not a new paradigm. Through the lens of a traditional approach, the CAF uses the Operational Planning Process (OPP) combined with operational art and design for the creation of military strategies and operations within the contemporary operating environment. This raises the following question: What role does contextual understanding have in producing CB strategies and operations that are effective and measurable? This monograph argues that the use of system framing from a linear to a complex adaptive systems approach can become the basis for CB operations that address national imperatives and mission requirements. The CAF needs to institutionalize a methodology, a constructivism approach that includes design thinking within its OPP. Without the institutionalization of a holistic method for planning and executing CB operations, the CAF will continue the same traditional approach of "bottom-up" through intensive iterations. Design thinking allows commanders and their staff to create internally coherent military strategies, campaigns, and operations, in line with the twenty-first century's requirements to be externally relevant. This methodology will help the CAF to avoid the same painful learning, adaptation, and evolution that the organization experienced in the past while conducting CB operations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.019 | 0.011 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.002 |
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".