Performing counter-terrorism: Police newsmaking and the dramaturgy of security
Bibliographic record
Abstract
Expansive domains of counter-terrorism policing remain buffered from popular visibility, and police organizations remain primary definers of security threats and the police work involved in controlling these threats. Examining the interface between police image work and the continued intensification of the “war on terror,” this article details how police agencies stage police raids, arrests, and press conferences in efforts to frame terrorism narratives in Canada; a police dramaturgy that shapes how the public consumes news about the threat of Islamic terrorism and the pre-crime interventionism of policing and security agencies. To examine these police newsmaking practices, two approaches are utilized: first by detailing experiences of defence lawyers who have worked on high-profile cases, then through an analysis of declassified documents related to the preparation and roll-out of a high profile national press conference to narrate the interdiction and killing of prospective terrorist Aaron Driver. Contributing to debates on police image work and contemporary debates around police power, this article demonstrates how policing agencies curate the image of counter-terrorism through newsmaking practices that exaggerate the threat of terrorism, shape the public imaginary around the threat of Islam, refurbish the role of police as symbolic guardians against evil, and aim to reproduce securitarian politics that advocate for more pre-emptive and surveillance powers.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.021 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".