Bibliographic record
Abstract
In order to understand criminal legislation, one needs to refocus from criminal legislation to its most modern form, the code ─ by turning one's historical attention to the significance of criminal codes, thereby reconnecting the analysis of law to the analysis of the state, jurisprudence to politics. Therefore, particular attention needs to be provided to two analytic distinctions ─ between private and public law, and between criminal and civil law. Modern criminal law scholarship fails to recognize that its subject in large part no longer represents a species of law at all. The category mistake, in other words, transcends that of law and extends to the range of coercive methods available to the modern state. Insofar as criminal law has been transformed into a mode of regulation, it has been transformed into a species of police, rather than of law. Not only the distinction between public and private law remains unclear and unexplored in English Criminal Law scholarship, so does the definition of law and its differentiation from other modes of state coercion. It is no surprise, then, that the greatest successes of English criminal codification would be criminal codes drafted by wise Englishmen (some of whom were experts in criminal law [Stephen], others not [Macaulay]) for various colonies, Canada and India in particular.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.091 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".