Chapter 7 – Curing Complexity: Moving Forward from the Toronto 18 on Intelligence-to-Evidence
Bibliographic record
Abstract
This chapter addresses one aspect of Canada’s “intelligence to evidence” (I2E) problem that both featured in the Toronto 18 prosecutions and has since occupied courts (and presumably agencies): criminal trial challenges to warrants supported by intelligence and used to collect information employed either to seed a subsequent RCMP investigation (or wiretap warrant) or as evidence of guilt in a subsequent prosecution. These matters implicate so-called Garofoli applications. The awkward interface between these Garofoli applications and I2E may constitute the single most perplexing (and possibly resolvable) I2E issue. Specifically, this chapter asks whether Garofoli applications heard ex parte (that is, with only the government party before the court) and in camera (that is, in a closed court) would be constitutionally viable under section 7 of the Charter. We conclude that closed material Garofoli applications with built-in procedural protections — namely statutorily-mandated special advocates — would meet constitutional standards.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.048 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".