Prioritizing Defence Industry Capabilities: Lessons for Canada from Australia
Bibliographic record
Abstract
A number of Canadian acquisition announcements over the past few years have generated significant debate about a variety of issues like whether or not Canada should have a separate procurement agency, whether or not industrial and regional benefits are appropriate and whether or not Canadian companies should be given preference over international companies. In discussions about improving our procurement process Australia is often used as an example because the nations are generally considered to be similar in size with respect to GDP, population and military. This study examines Australia’s approach to establishing a defence industry policy with a set of Priority Industry Capabilities and how that policy connects with military procurement in order to identify those lessons that might be useful for Canada as it seeks to improve its own procurement process and its relationship with the defence industry. The study looks at some important background information on the Australian experience and then looks more specifically at the most recent articulation of policies in Australia. Although Australia is not without its own challenges, there are a number of lessons that stand out for Canada. This study discusses the lessons for Canada and recommends that government spends the time and effort required to connect a series of related policy documents that provides industry and others with an articulation of what the government of the day intends to do and what their priorities are moving forward. It also recommends a holistic review of the entire procurement process to determine what is working well and what actually needs fixing would be useful.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".