Chapter 5 – The Canadian Security Intelligence Service and the Toronto 18 Case
Bibliographic record
Abstract
While it is well known that the Canadian Security Intelligence Service (CSIS) played a key role in the investigation of the Toronto 18 cases, these activities have been left out of the public record. To provide some context for the other contributions in this study, this chapter proceeds by describing the process by which CSIS conducts counter-terrorism investigations – from initial notification of the threat through to cooperating with the RCMP. Although there have been some changes since the mid-2000s, these processes largely remain in place today. Importantly, while the case of the Toronto 18 was seen as a huge success for Canada’s counter-terrorism capabilities at the time, it also shaped expectations regarding how future threats would be treated. Canadian national security would spend much of the five to seven years after the Toronto 18 arrests looking for the next such group, a threat that never really manifested. In this way, the Toronto 18 may have contributed to bias in understanding an evolving national security threat that was manifesting in the form of lone actors and extremist travel.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".