Positive Obligations and Criminal Justice: Duties to Protect or Coerce?
Bibliographic record
Abstract
This chapter explores the relationship between criminal law, criminal process and human rights from a slightly different perspective. It demonstrates that while human rights may well be used to limit the excesses of security and law and order politics, the nature of the relationship between human rights and criminal justice cannot be captured alone by the view of rights as a limit on the coercive reach of the criminal law and criminal justice institutions. Increasingly, human rights, cast as positive rights, have resulted in claims for the extension of the criminal law, the creation of preventative duties or ‘protective policing measures’, for the intensification of policing and prosecution of sexual and violent crimes in particular, and threats to security or public protection in general. The story is a complex one which is intimately linked to the growing international acceptance of human rights as including positive rights, and hence a shift from a conception of rights as a limitation on State action to one which now views rights as demands for such action. The result is a process whereby the human rights of those subject to harm - such as the right to life, the right against torture, inhuman and degrading treatment, the right to private life, the right against discrimination, and the right to security - have now combined to create what I argue are most accurately described as coercive duties on the State to criminalize, prevent, police and prosecute harmful acts. We need to remain vigilant about how these positive duties are framed and deployed to legitimate such coercive action from the State.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.043 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".