Politics and intelligence analysis: the Canadian experience
Bibliographic record
Abstract
Academic debate on the interplay between politics and intelligence is dominated by the U.S. experience. Our research, based on interviews with over sixty individuals in the Canadian intelligence and national security community and including political staffers, provides a new case study: that of Canada, a middle power with considerable access to intelligence through the Five Eyes partnership. We found that cases of hard politicization of intelligence analysis are virtually non-existent in Canada. The most important factor explaining this finding is Canada’s structural position in the world, or how its geography shapes the broader context of interactions between intelligence and politics. Beyond this, six more specific factors at the domestic level also matter: the relative unimportance of foreign and security policy as political issues, few opportunities, a lack of political benefits, low intelligence literacy generally among policy makers, poor transparency in national security decision making, and a tradition of non-partisanship in the civil service. The paper concludes by reflecting on this assessment: while hard politicization remains a rarity in Canada, the shields that have prevented the emergence of politicization will likely be increasingly tested in the future.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.067 | 0.019 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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".