Terror carceralism: Surveillance, security governance and de/civilization
Bibliographic record
Abstract
Following the first 15 convictions under the 2001 Anti-Terrorism Act, Canadian penal authorities developed new surveillance and security practices targeting this small population of prisoners. Primarily, reforms ensure that terror convicts are labelled as highly dangerous offenders, despite any of them having participated in attacks or violence. Focusing on the Canadian context, this article contributes to recent scholarship by discussing how these emergent penal practices are informed by a convergence of domestic anti-terrorism policies and a networked field of counter-terrorism experts; what Bigo calls (in)security management professionals. Detailing recent debates and reforms, I focus on emergent ‘deradicalization’ strategies that are informed by transnational ‘working groups’ and (in)security management actors. I argue that these increasingly illiberal counter-terrorism practices are framed as measures of social defence against the enemies of western civilization. Yet, paradoxically, these emergent security practices are antithetical to the ‘civilizing process’ of penal modernism, further displacing discourses of rehabilitation or reintegration and entrenching a ‘criminology of the other’. I conclude by discussing what I call new practices of terror carceralism, which represent an entrenchment of vengeance and retribution, justifying a host of invasive surveillance and security measures against those caricaturized as captured terrorist others.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.031 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".