Bibliographic record
Abstract
In the Principles of the Penal Code, Jeremy Bentham described offences that he labelled presumed or evidentiary. The conduct penalized under such offences is punished not because it is intrinsically wrong, but because it probabilistically indicates the presence of an intrinsic wrong. Bentham was sceptical of the need to create offences, but grudgingly accepted their value in light of deficiencies in procedure and the judiciary. These days the scepticism is even greater, with courts and commentators in the United States, Canada, the United Kingdom and elsewhere believing that such ‘proxy’ offences deny a defendant the right to establish that he did not engage in the conduct that the presumed offence probabilistically but not necessarily indicates. On closer analysis, however, such scepticism appears unjustified. Almost all offences, and indeed almost all legal rules, are premised on a probabilistic relationship between the behaviour the rule encompasses and the behaviour that is the rule-maker's real concern. Presumed offences may make this relationship especially obvious, but it is a relationship that exists whenever the law operates by the use of rules.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".