Criminalising Contagion: Legal and Ethical Challenges of Disease Transmission and the Criminal Law.
Bibliographic record
Abstract
The use of the criminal law to punish those who transmit disease is a topical and controversial issue. To date, the law (and the related academic literature) has largely focused on HIV transmission. With contributions from leading practitioners and international scholars from a variety of disciplines, this volume explores the broader question of if and when it is appropriate to criminalise the transmission of contagion. The scope and application of the laws in jurisdictions such as Canada, the United Kingdom and Norway are considered, historical comparisons are examined, and options for the further development of the law are proposed. Contributors from the UK, the United States, Norway, New Zealand and Canada provide international perspectives on the issues raised Contributors include academics from a range of disciplines, scholar-activists and a healthcare professional Provides a topical consideration of the challenges raised by the use of criminal law in the context of disease transmission
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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 | 0.021 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.074 |
| Scholarly communication | 0.022 | 0.014 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.023 | 0.021 |
| Insufficient payload (model declined to judge) | 0.003 | 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".