From probabilities to possibilities: terrorism peace bonds, pre-emptive security, and modulations of criminal law
Bibliographic record
Abstract
Scholars have noted that pre-emptive security practices have gradually been transforming the probabilistic logics of criminal law towards increasingly possibilistic logics. Our article is focused on the terrorism peace bond regime in Canada since 2015, which provides an explicit illustration of the movement from probabilistic to possibilistic thresholds in criminal law. Documenting specific experiences of terrorism peace bond proceedings through the narratives of defence lawyers involved in recent cases, we focus on several manifestations of possibilistic practices; including the difficulties of contesting accusations about future activities, the erosion of evidentiary standards, conjectural reasoning animated by the racialized character of the ‘war on terror,’ and a reverse onus placed on accused subjects in these proceedings. Contributing to research examining the transformations of criminal law, we suggest that terrorism peace bonds are not an exceptionalist practice but a modulation that allows previously excluded legal norms into a broadened, more authoritarian umbrella of criminal law. To conclude, we position terrorism peace bonds not so much a return to the criminal justice model but as a possibilistic modulation of criminal law that accommodates pre-emptive and racialized practices in more depoliticized forms.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.057 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".