What Canada could learn from U.S. defence procurement: Issues, best practices and recommendations
Bibliographic record
Abstract
Despite differences in scale, Canada and the U.S. face common challenges in military procurement and there is much Canada can learn as both countries pursue reforms. The U.S. employs a system of systems approach, based on requirements, resource allocation and acquisition. The process begins with the Joint Capabilities and Development System, focused on identifying and prioritizing needs and assessing alternatives. This is followed by the Planning, Programming, Budgeting and Execution System, which leads to the creation of a budget and provides guidance for the project’s execution. The third and final step is the Defense Acquisition System, which oversees the development and purchase of the new equipment. While deceptively simple in summary, U.S. defence procurement is dogged by problems — particularly cost overruns, a surfeit of key players and delayed schedules which degrade troops’ performance in the field. Additionally, the defence products market is restricted, inevitably limiting competition, encouraging misbehaviour on the part of business and driving up prices. The DoD is in the midst of consultations with contractors and Congress is undertaking an effort to rewrite acquisition laws. But the most pressing questions remain: Does a best procurement practice exist? If so, what criteria define it? In light of Canada’s new Defence Procurement Strategy (DPS), some lessons are clear. Further analysis is needed to figure out whether reforms can succeed in so narrow a marketplace. More attention must be paid to shaping contracts and clarifying expectations about sticking to schedules. And Ottawa must think carefully about the military’s needs, as it pushes ahead with the DPS. In surveying change at the DoD, this brief draws pointed conclusions to which Canada’s defence planners must pay heed, if they’re to leave the military stronger than they found it.
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 | 0.022 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".