General Defences in Criminal Law: Domestic and Comparative Perspectives
Bibliographic record
Abstract
Contents: Introduction. Part I: How criminal defences work, William Wilson Avoiding criminal liability and excessive punishment for persons who lack culpability: what can and should be done?, Bob Sullivan Prior fault: blocking defences or constructing crimes, J.J. Child Transfer of defences, Michael Bohlander Consent in the criminal law: the importance of relationality and responsibility, Jonathan Herring Good and harm, excuses and justifications, and the moral narratives of necessity, Susan Edwards Duress and normative moral excuse: comparative standardisations and the ambit of affirmative defences, Alan Reed Of blurred boundaries and prior fault: insanity, automatism and intoxication, Arlie Loughnan and Nicola Wake Mistaken private defence: the case for reform, Claire de Than and Jesse Elvin Statutory defences of reasonableness: inexcusable uncertainty or reasonable pragmatism, Christopher J. Newman How do they do that? Automatism, coercion, necessity, and mens rea in Scots criminal law, Claire McDiarmid In a spirit of compromise: the Irish doctrine of excessive defence, John E Stannard. Part II: Australia, Mirko Bagaric Canada, Kent Roach France, Catherine Elliott Germany, Kai Ambos and Stefanie Bock Islamic law, Mohammad M. Hedayati-Kakhki The Netherlands, Erik Gritter New Zealand, Julia Tolmie South Africa, Gerhard Kemp Sweden, Petter Asp and Magnus Ulvang Turkey, R Murat Onok United States of America, Luis E Chiesa. Index.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".