Canadian Defence Commitments: Overview and Status of Selected Acquisitions and Initiatives
Bibliographic record
Abstract
For years, successive Canadian governments have been overpromising and under-delivering on defence procurement. Timetables have slipped even as repair and maintenance costs for aging equipment have soared, while elaborate rules have obscured the acquisition process in a bureaucratic fog. This paper assembles information from a wide range of official sources and cuts through the confusion. It surveys 15 Canadian defence acquisitions and initiatives, each anticipated to cost more than $100 million, to account for the delays. Final replacements for the ancient Sea King helicopters are no closer to arriving — after almost 30 years — because the DND failed to recognize that it asked for technology that is still in development. The Joint Support Ship project is years behind schedule because, as originally conceived, it sought to integrate so many capabilities that it was unbuildable. The Integrated Soldier System Project is almost as far behind because Ottawa’s procurement rules are so complex and niggling that no bidder could fulfill every single one. Canada faces evolving threats, but efforts to equip the Canadian Forces to meet them have been marked by a long litany of failures — failures of communication, of organization and of vision. This paper sets out the military procurement process, and concisely explains the most egregious flaws, making it essential reading for anyone interested in the future of Canada’s military.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.027 | 0.068 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".