Bibliographic record
Abstract
Antony Duff’s new book, The Realm of Criminal Law (Oxford University Press, 2018), is a magnificent achievement. It is at once a restatement, clarification and—in some important respects—a departure from his previous work. The Realm of Criminal Law sketches a new framework—citizenship within a legally constituted civil order—in which familiar Duffian ideas of relational liability, public wrong, and answering for crime are given a fresh reconceptualization. Most importantly, while Duff has for a long time emphasized the criminal law’s public character, this book is Duff’s most thorough discussion to date of how the political and retributive dimensions of his thinking about criminal law are related. First a personal note: Duff’s work on the philosophy of criminal law—I am thinking particularly of Intention, Agency & Criminal Liability and his book on attempts—was what first convinced me that there was such a thing as philosophy of criminal law and that it can be a worthwhile activity. I am gratified to be invited to contribute to the discussion Duff has opened up with his new book, even more so because this book represents his most sustained engagement with the political turn in the philosophy of criminal law, a topic in which I am somewhat invested.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.008 | 0.035 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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".