Analysis: Military Platform Procurement Strategies and the Need for Political and Doctrinal Alignment in Type 4 Purchases
Bibliographic record
Abstract
Abstract Each procurement situation has different payoffs and costs for governments, both administratively and politically. Large-scale procurement, given its duration and size, has the possibility of highly uncertain benefits along with potentially high costs. The Type 26 frigate and F-35 aircraft cases in Canada and Australia show that for successful procurement/implementation to occur what is needed is a clear set of objectives and targets established by a government which can then be matched to specific tools and tool calibrations by delivery departments or agencies and for this congruence to be maintained over time and through changes in government. The Canadian case studies demonstrate how the beginning of the twenty-first century brought with it rapidly shifting strategic priorities that left the CAF in difficulty due to its inflexible forces’ doctrine within the context of changing government strategic goals and objectives in the post-Cold War period. In Australia, however, dealing with the same weapons systems at the same time, the alignment of government policy and forces’ doctrine was maintained, resulting in projects proceeding faster, and with all-party agreement, than was the case or existed in Canada.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.002 |
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".