Chapter 6 – Navigating National Security: The Prosecution of the Toronto 18
Bibliographic record
Abstract
Prosecutions of terrorism cases pose unique challenges because they typically raise complex issues engaging the right of an accused person to disclosure of relevant material and the public interest in protecting national security. This chapter provides the lead prosecutor’s perspective on the Toronto 18 prosecution, some of the disclosure issues that arose in that case, and how similar issues might be handled in the future. Part II provides an overview of the Toronto 18 investigation. Part III reviews the Canadian disclosure regime in the context of terrorism prosecutions, contrasts it with disclosure regimes in the U.K. and the U.S.A., and highlights some problems associated with the current bifurcated approach when the defence seeks to compel disclosure of sensitive information. Part IV discusses how the prosecution in the Toronto 18 approached the disclosure of information in CSIS holdings. Part V concludes with a discussion of how the prosecution managed its disclosure obligations in the context of the Garofoli review of the wiretap authorizations, and how similar issues might be handled in the future given subsequent developments in the law.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| 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".