Positive Obligations and Criminal Justice: Duties to Protect or Coerce?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This chapter explores the relationship between criminal law, criminal process, and human rights from a slightly different perspective. It seeks to demonstrate 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. The chapter is organized as follows. It starts by outlining key areas where positive rights claims have shaped the criminal law and criminal justice process. It then examines the relationship between positive rights and coercion, and critiques the language used to frame certain positive duties. Finally, the right to security is used as a case study through which to demonstrate the concerns raised by the development of coercive duties.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it