Bibliographic record
Abstract
In Philip K. Dick’s short story Minority Report, the institution of Precrime punishes people with imprisonment for crimes they would have committed had they not been prevented. With Dick’s allegorical inspiration, the authors of Criminal Law and Precrime: Legal Studies in Canadian Punishment and Surveillance in Anticipation of Criminal Guilt posit that recent developments in Canadian law indicate a trend toward imposing punitive measures at increasingly earlier stages of the prosecutorial process. The result is a potentially new field of criminal management that could be characterized as "precrime"—particularly the use of the law as a technology of surveillance and prevention since "terror" became a justification for intervention. The authors note that as risk management logics (based in actuarial sciences) have shifted to precautionary ones (based in administrative sciences), the law has responded by developing techniques in the arena of criminal regulation in light of the "war on terror": the need to ensure security, the proliferation of digital data, and the development of drones, social networking, and cloud storage to gather personal data. The authors view shifts in criminal investigation; the substantive criminal law of sexual expression, conduct, and work; and civil forfeiture as emblematic of precrime populism. The unifying theme of these techniques is that they occur prior to state-identified crime, arise out of a precautionary philosophy, and seek to presume (or circumvent) criminality. The book is a provocative read for scholars and students in criminal law, policing, and surveillance, as well as for those interested in how areas of law, such as immigration, health, and anti-terrorism, are mobilizing the logics of risk and surveillance in new ways that emphasize precaution. The authors invite legal scholars to place the analytical lens of precrime on criminal and regulatory practices in Canada as well as other Western nations across the globe.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".