Bibliographic record
Abstract
A statement like ‘a person who commits a crime can be prosecuted, convicted, and punished by the government’ is comprehensible only with a host of assumptions, each of which may be questioned. For instance, why does there have to be a response at all by anyone to the crime? Why is it that the government – and not a private individual or individuals – does the responding? Why is it a particular government and not some other one? Why is it that this particular process is called the criminal process, and why does it look the way it does? Why is it that this particular response is called ‘punishment,’ and why does it look the way it does? And is there another way altogether? Can we imagine changes, both big and small? How should we go about thinking about such changes? None of these questions are easy to answer. There are many different ways to go about answering them, and fierce disagreements are to be expected. However, there are few works out there that are as intelligent, coherent, and comprehensive at addressing these questions as R.A. Duff’s The Realm of Criminal Law. This review essay describes the main argumentative thread of the book, raises some questions, offers a few remarks, and ends with the suggestion that the book may be fairly read as an abolitionist text.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.060 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".