4.3.3 Capability Engineering within Canadian Defence: Experimentation and Lessons Learned
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This paper summarizes the results of an experimental evaluation conducted to assess the Capability Engineering approach developed for the Canadian Forces/Department of National Defence to facilitate decision‐making on strategic investments and divestments. This work was performed as part of the Collaborative Capability Definition, Engineering and Management Technology Demonstration Project (CapDEM TDP). Based on the systems engineering paradigm, the approach is articulated around three axes: People, Process and Materiel. The focus of this paper is on the lessons learned from Exercise Gamma, the last of three validation exercises which are part of an evaluation strategy attempting to evolve Capability Engineering from theory into practice. Lessons learned have helped to identify specific improvements as well as critical success factors for the implementation and application of Capability Engineering.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it