Canada v. Asad Ansari: Avatars, Inexpertise, and Racial Bias in Canadian Anti-Terrorism Litigation
Bibliographic record
Abstract
This essay examines the case of Asad Ansari, who was 25 years old at the time of his trial as part of the so-called Toronto 18. Through a close examination of certain aspects of his case, this essay will show that rather than Asad Ansari, what was on trial was an avatar of Ansari, which took shape through the explicitly inexpert and implicitly racially biased litigation of Islam itself. The very structure of the litigation collapsed Islam, the religion, into the defendant. The absurdity of this absent expertise is pregnant in the facially neutral, but substantively suspect, procedural structure of the litigation via the form of evidentiary motions and the use of leading questions on cross-examination. This procedural structure was substantially suspect in the case of Ansari because utterly inexpert testimonies and biased perspectives were permitted by the very structure of Canada’s adversarial system of justice. From the accused Ansari, to the government prosecutors, and even to the government paid confidential informants, no one was disinterested in the outcome of the trial. Yet none were duly certified by the court as impartial experts on Islam, jihad or the regional conflicts in Iraq, Syria or Afghanistan, despite all of them testifying about such matters as proxies for the defendant’s state of mind. Nor did the presiding judge Justice Fletcher Dawson—in the role of the paternal (if not patronizing) overseer of the jury—recognize the relevant parties were litigating matters outside their personal and institutional competency. The analysis below suggests that Ansari was found guilty not because he contravened anything that would fall within the anti-terrorism legislation. Rather, his guilt is premised upon the fact that he read, reviewed, and thought about ideas that the security state considers radical and even threatening. Because those ideas were embedded in propaganda from groups like al-Qaida, the Taliban, and Iraqi insurgencies, ultimately the person of Ansari was collapsed into these hard to find and harder to defeat groups.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".