Bibliographic record
Abstract
Flaws in Canada’s military procurement processes are a perennial burden on both government and industry. The release of the Defence Procurement Strategy this year signified Ottawa’s inclination toward change. Among other things, the DPS promises to consult industry and outside experts earlier, create a specialist acquisition branch within Public Works & Government Services Canada and use military equipment projects to generate domestic jobs and growth. The DPS is a good start, but more focused solutions are required. The defence market has too few buyers and sellers to be truly competitive — especially in Canada. Government must share information with industry at every step, clearly and comprehensively, if Canadian firms are to win contracts fairly, keeping the economic benefits at home. A sweeping, end-to-end review of procurement is also required to identify current practices that work and others in need of improvement. Preliminary cost estimates can’t be too firm because prices shift as projects develop, and all too often, capabilities are downgraded in response. Government has to be transparent about how far into the future lifecycle costs run, or stop trying to establish them altogether, so as to avoid the consequences of embarrassingly unrealistic assessments. A separate procurement organization should also be established outside of the DND and PWGSC to make better use of the people with the skills to run complex military procurement projects. Canada’s military procurement system is not as broken as its most strident critics allege, but it is coming under increasing fiscal and policy pressures. This brief fleshes out the issues that would-be reformers should take into account and surveys procedures among allied nations to offer a roadmap for change.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".