Capability engineering: transforming defence acquisition in Canada
Bibliographic record
Abstract
Capability engineering, a new methodology with the potential to transform defence planning and acquisition, is described. The impact of capability engineering on existing defence business processes and organizations is being explored in Canada during the course of a four-year Technology Demonstration Project called Collaborative Capability Definition, Engineering and Management (CapDEM). Having completed the first of three experimentation spirals within this project, a high-level capability engineering process model has been defined. The process begins by mapping strategic defence guidance onto defence capabilities, using architectural models that articulate the people, process and materiel requirements of each capability when viewed as a system-of-systems. For a selected capability, metrics are rigorously applied to these models to assess their ability to deliver the military capability outcomes required by a set of predefined tasks and force planning scenarios. By programming the modification of these tasks and planning scenarios over time according to evolving capability objectives, quantifiable capability gaps are identified, that in turn drive the process towards options to close these gaps. The implementation plan for these options constitutes a capability evolution roadmap to support defence-investment decisions. Capability engineering is viewed as an essential enabler to meeting the objective of improved capability management, subsuming the functions of capability generation, sustainment and employment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".