Bibliographic record
Abstract
H.L.A. Hart’s insight, that some people may be guided by an offence provision because they take it as authoritative and not merely to avoid sanctions, has had an enormous influence upon criminal law theory. Hart, however, did not claim that any person in any actual legal order in fact thinks like the “puzzled man”, and there is lingering doubt as to the extent to which we should place him at the center of our analysis as we try to make sense of moral problems in the criminal law. Instead, we might find that our understanding of at least some issues in criminal law theory is advanced when we look through the eyes of Holmes’ “bad man”. This becomes clear when we consider the respective works by Hart and Douglas Husak on overcriminalization, James Chalmers and Fiona Leverick’s recent discussion of fair labeling, and Meir Dan-Cohen’s classic analysis of acoustic separation. These works also suggest, in different ways, that an emphasis on the bad man can expose the role of discretion in criminal justice systems, and the rule of law problems it generates.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".