The politics of military procurement: the F-35 purchasing process in Canada and <i>Australia</i> Compared
Bibliographic record
Abstract
The willingness of defence departments to select the F-35 Joint Strike Fighter (JSF) for their fifth-generation multirole fighter has frequently been analyzed as stemming from the close historical connections allies such as Japan or Canada have with the United States. However, such an approach glosses over or ignores the operation of military procurement processes which are more idiosyncratic and subject to many pushes and pulls from different actors and directions. This article compares the experiences of Australia and Canada in procuring the JSF. Both countries are British Commonwealth members, with a long history of supporting western, and in particular, US alliances. But while Australia has secured its F-35 procurement and the Royal Australian Air Force (RAAF) has already received its F-35s, Canada has only recently overcome a lengthy F-35 procurement battle that remains mired in controversy and will not deliver to the Royal Canadian Air Force (RCAF) an aircraft for several years yet. This comparative case study between Australian and Canadian defence priorities offers a new explanation for this disparity of procurement success based on the need to both create and maintain alignment between government strategic defence policy and military service doctrine if major platform purchasing decisions are to survive.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".