Racing Madness: The Terrorizing Madness of the Post-9/11 Terrorist Body
Bibliographic record
Abstract
In this chapter, I explore several key questions: What does the circulation of the figure of the “mad Muslim terrorist” do for secular-liberal Western democracies and their subjects? What needs to be in place for this figure to emerge in particular ways post-9/11? What kind of political rationalities are mobilized to legitimize extra-juridical incarceration of these “mad Muslim terrorists”? How does racial logic intersect with popular psy discourse in the emergence and circulation of the figure of the “mad Muslim terrorist”? I explore these questions in relation to a newspaper article entitled Insanity and Terrorism by Stewart Bell (2004) in the National Post , a nationally distributed right-leaning newspaper in Canada with a daily readership of 1.3 million, including online posts (National Post Staff 2012). Bell is an award-winning, popular Canadian journalist and senior reporter for the National Post where he regularly contributes columns on terrorism. His last two books dealt with terrorism in Canada, and his articles on terrorism have appeared in several magazines across the country. He is hailed as one of Canada’s “foremost reporters on terrorism.” A quick examination of his major publications reveals the racist “Canada as a safe haven for terrorists” logic that has been mobilized to legitimize the draconian antiterrorism (read anti-Muslim) legislation and security regime post-9/11 (Patel 2012). Therefore, while I am investigating just one of Bell’s articles, I want to emphasize that the discourse of the “mad Muslim terrorist” is not unique to this article, but offers an opening for studying complex questions about how the resistance and political movement of bodies produced at the nexus of race/religion and madness become recognizable figures of danger in our current geopolitical context. 1 These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".