3.4.2 Capability engineering for strategic decision making
Bibliographic record
Abstract
Abstract The current paper summarizes the Capability Engineering Process (CEP) being developed to help decision making on strategic investments and divestments for the Canadian Forces and Department of National Defence. This effort is part of a technology demonstration effort called the Collaborative, Capability, Definition, Engineering and Management (CapDEM). The CEP introduces ways to increase strategic agility capability management in a world in constant evolution. A CEP application provides a set of options addressing a given capability gap. Among benefits, this process: (1) provides decision makers with timely strategic information through an iterative and incremental approach; (2) reduces time spent on unrealistic options by continuously pruning the solution space as early as possible; (3) provides operationally acceptable strategic options with direct involvement of the operational community into the solution development; and (4) provides feasible options by ensuring commitment and participation in developing solutions involving all of the organization's functional components: Personnel, R&D, Infrastructure, Concept development, Information management, and Equipment (known as PRICIE components in Canada).
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".