Increasing Canada's Foreign Intelligence Capability: Is it a Dead Issue?
Bibliographic record
Abstract
Despite the fact that the issue of whether Canada should develop a greater foreign intelligence capability has been broached numerous times, in various guises, over more than a century, those who have followed the development of the country's intelligence architecture will know it has never had a foreign intelligence service like its close allies. They will also be aware that on each occasion on which the issue has been raised, the Canadian government has declined to proceed. If history is any guide, there is a strong likelihood that the idea of Canada developing a more robust capability will again engage politicians, former intelligence officials, academics, the media, and think tanks in the not too distant future. The view adopted in this paper is that the public discourse has become sterile, and that if it is to advance, aspects of the counterfactual case – why has a foreign Humint capability not been developed? – may prove more fruitful.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".