In Conversation with Mubin Shaikh: From Salafi Jihadist to Undercover Agent inside the "Toronto 18" Terrorist Group
Bibliographic record
Abstract
This interview with former undercover agent Mubin Shaikh can help academics and security practitioners understand the key role played and the challenges faced by covert human intelligence sources within domestic terrorist groups. The interview highlights the identity crisis, the personal factors, and the allure of jihadi militancy that initially drove Shaikh to join a Salafi jihadist group. It investigates Shaikh’s process of disengagement from the Salafi jihadist belief system and his rediscovery of a moderate, inclusive, and benevolent form of Islam. It explores his work as an undercover agent for the Canadian Security Intelligence Service, the Royal Canadian Mounted Police, and the Integrated National Security Enforcement Team responsible for disrupting domestic terrorist groups. The “Toronto 18” terrorist cell, the key role played by undercover agents in preventing terrorist action, and the challenges posed by entrapment are also discussed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.033 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".