Are we Flipping Coins with the Liberty of Potentially Dangerous Individuals?: A Comparative Analysis
Bibliographic record
Abstract
The concept of dangerousness in Canadian and French criminal law is a central component in the development of prophylactic measures, such as section 810.1 and 810.2 of the 'Criminal Code' and similar French provisions. The imposition of preventive measures to control the risk of future behaviour of potentially dangerous individuals relies on inexact science to determine and assess dangerousness. In the last decades, several risk assessment tools have been developed, notably some in Canada, but their reliability in predicting dangerousness varies. The objectivity and reliability of a determination of dangerousness can be affected not only by the type of risk assessment tool used by clinicians in the assessment of dangerousness, but also by factors such as the procedural setting in which the evidence is treated. When compared to the Canadian scheme, the French non-adversarial setting offers new alternatives and procedural safeguards in determining and controlling dangerousness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".