Intelligence Analysis and Policy Making: The Canadian Experience <i>by Thomas Juneau and Stephanie Carvin</i>
Bibliographic record
Abstract
Thomas Juneau and Stephanie Carvin have produced an excellent and unique contribution to the intelligence studies field. Compared with research books on aspects of the U.S. intelligence community and perhaps to some extent Australia's intelligence community, works on Canada are still rare, and empirical studies such as this even rarer. The central theme is the role of intelligence analysis in policy-making within Canada from a contemporary institutional rather than “how to” perspective. The methodological approach draws on sixty-eight interviews with intelligence and policy-maker staff at all levels both current and former, which, along with the detailed analysis of secondary sources, provides an impressive foundation to explore how intelligence analysis has been used—or as they authors argue not used sufficiently by policy-makers in Ottawa. The semistructured interview approach used here is one I have used several times in my own research on intelligence governance and organizational reform across the “Five Eyes” intelligence communities over the last decade (see for example, Intelligence and Intelligence Analysis). Armed with a coherent methodological approach, the book thematically explores via five chapters the many structural/cultural barriers and even what they describe as sudden threats (or “electro-shock”) events that sometimes have resulted in improved connection between Canada's intelligence enterprise and the policy-makers it serves. All five chapters provide a coherent analysis of relevant themes, though I think chapter five (“Recommendations and the Way Ahead)” is the richest in terms of how these applied researchers offer prescriptions to the many problems of why intelligence analysis and products continue to be unseen as vital to policy deliberations in many parts of the government of Canada. The authors offer several useful recommendations for improving the overall governance and structure under which intelligence analysis occurs within agencies and across the intelligence community. For example, the focus on strengthening the analytical capabilities, leadership, and coordination of the Intelligence Assessment Secretariat (IAS) in a similar vein to what occurred in Australia (2018) when the Office of National Intelligence (ONI) was established could be a critical way forward for helping improve intelligence analysis excellence and influence with policy-makers. But as my own research suggests, the ONI after four years is still a work in progress on a range of the intelligence leadership and coordination functions it has responsibility for. Though as the authors rightly point out, Canadian intelligence reformers need to learn the good and bad lessons from Australia and be mindful of not “australiadizing” Canberra's solutions to Ottawa's problems (176). Other useful though potentially equally costly suggestions for improving intelligence analysis influence in the policy context included the long-debated consideration of Canada creating its own foreign (HUMINT) intelligence agency, and in the interim, CSIS taking up a greater role in foreign intelligence collection. There are also many other good ideas, but as the authors say, intelligence has not been a priority for policy-makers and any reforms have only occurred in “fits and starts” (172). But Canada's immediate and global security environment is now much less benign, and what is likely needed is a comprehensive independent review of the intelligence community—similar to the regular five yearly ones in Australia that take stock of the ever-changing security environment and how holistically meaningful reforms can take place. The authors show us in detail what recommendations in the area of intelligence analysis an independent reviewer would find useful. An excellent and timely contribution.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.014 |
| Science and technology studies | 0.073 | 0.039 |
| Scholarly communication | 0.021 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 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".