Bibliographic record
Abstract
As the government of Canada cuts back on defense spending after years of significant increases, critics lament the supposed lack of a “grand strategy” when it comes to military expenditures. But the current reductions are actually a return to traditional Canadian grand strategy, albeit one that is not that “grand.” Put in retail shopping terms, Canada has tended to follow an economizing Walmart approach to defense spending as opposed to a more upscale Saks Fifth Avenue style. Though often criticized as nothing more than “free riding,” this approach may be more accurately described as “easy riding.” It is one that was deliberately and carefully chosen by successive Canadian policy makers, acting in accordance with “realism Canadian style.” It allowed the country to achieve security at home and to use the justifiably highly regarded Canadian Armed Forces to participate in a limited, yet effective and internationally appreciated manner in overseas military engagements as a stalwart Western ally without endangering the economy and social programs by spending more on defense than was absolutely necessary. While the Walmart approach can be taken too far, in these times of fiscal austerity when national budgets are difficult to balance without cutting defense spending and when interventionist exhaustion is afflicting many Western governments, including the United States, the lessons from the Canadian experience should resonate with policy makers and analysts well beyond 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 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".