Litigating Informer Privilege Under Section 37 of the Canada Evidence Act: A Critique of R. v. Basi
Bibliographic record
Abstract
Litigating informer privilege under section 37 of the Canada Evidence Act is a problematic practice that evinces dubious, expedient statutory interpretation. This case comment critiques the Supreme Court of Canada's decision in R. v. Basi for allowing this practice to continue. The comment first reviews the facts in Basi, and the Court's reasoning, with a focus on how section 37 was deployed in the informer privilege context. It explains the procedural considerations motivating the Crown to harness section 37, should it obtain an unfavourable informer privilege ruling at common law. The comment then argues that section 37 structures a discretionary, interest-balancing process appropriate to public interest immunity, but unsuited to resolving class claims such as informer privilege. Legislative debates on section 37 provide scant indication that its drafters intended the provision to cover informer privilege. The comment then returns to critique the Basi decision in light of the above analysis, positing that the Court treated section 37 as a pliant screen through which any claim to secrecy — including informer privilege — may pass. It concludes by suggesting legislative reform to allow controlled, streamlined review of informer privilege claims, rather than distorting existing statutory provisions in an ad hoc effort to make the system work.
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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 | 0.020 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.026 | 0.035 |
| Scholarly communication | 0.020 | 0.005 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.027 | 0.030 |
| Insufficient payload (model declined to judge) | 0.002 | 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".