Military Partners by Senator Hugh Segal CDFAI Senior Fellow And
Bibliographic record
Abstract
An increase in global threats, failing states, and crisis prone regions around the world, coupled with shrinking defence budgets in the US, as well as budget cuts in Canada’s most loyal joint deployment partners – France, the UK, and The Netherlands – indicates there will be no less of a demand for Canadian deployable capacity over the next few years. In this context, understanding the ‘political ’ requirements for various kinds of deployments is important and a key planning area for defence policy makers as well as military and strategic practitioners. To better understand the future of Canadian military deployments it is first necessary to examine the historical context of our post-war NATO and UN deployments as well as Prime Minister Harper’s 2006 commitment that required all significant military deployments to have parliamentary approval. Each deployment since WW II has been unique. The command structures, intelligence-sharing, mission design and assigned areas of responsibility for Canada, whether under the UN or NATO, have varied by the deployment itself and the nature of the mission. This requires our political and command requirements to adapt to the mission at hand. But whether or not Canada will participate in future deployments relies on not just being able to
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 | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.156 | 0.070 |
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".