Threading the Needle: Structural Reform & Canada’s Intelligence-to-Evidence Dilemma
Bibliographic record
Abstract
This article canvasses the “intelligence-to-evidence” dilemma in Canadian anti-terrorism. It reviews the concept of “evidence”, “intelligence” and “intelligence-to-evidence” (I2E). It points to the legal context in which I2E arises in Canada. Specifically, it examines Canadian rules around disclosure to the defence: the Stinchcombe and O’Connor standards and the related issues of Garofoli challenges. With a focus on CSIS/police relations, the article discusses the consequences of an unwieldy I2E system, using the device of a hypothetical terrorism investigation. It concludes disclosure risk for CSIS in an anti-terrorism investigation can be managed, in a manner that threads the needle between fair trials, legitimate confidentiality concerns and public safety. This management system rests on three legs: • Manage the relevance “tear-line” so that crimes less intrusive on CSIS information holdings are preferred over ones that are more intrusive. This strategy requires applying a prosecutorial insight to those investigations and planning their conduct to not prejudice trials. I bundle this concept within the category of “collecting to evidential standards” and “managing witnesses”. • Legislate standards to create certainty from the murk of evidence law. Here, two innovations stand out: legislate O’Connor style third-party status for CSIS where: CSIS’s investigation is a bona fide security intelligence investigation; CSIS and police do not have full, unmediated access to each other’s files; and, CSIS does not take an active role in the police investigation. But do not build this legislated third-party status around rigid barriers on information-sharing. Second, legislate ex parte, in camera procedures for Garofoli challenges of CSIS warrants in which special advocates are substituted for public defence counsel. • Manage the public safety risk by creating a fusion centre able to receive investigative information from all-of-government and fully apprised of the public safety risks associated with an ongoing investigation (or parallel investigations). Ensure it includes representatives from all the services with legal powers to respond to threats. The fusion centre would not itself be an investigative body, and would have O’Connor-style third-party status, something that would not require legislation but which might benefit from it.
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 | 0.019 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.031 | 0.030 |
| Scholarly communication | 0.019 | 0.006 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 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".