Bibliographic record
Abstract
The United States Supreme Court has repeatedly insisted that what distinguishes a criminal punishment from a civil penalty is the presence of a punitive legislative intent. Legislative intent has this role, in part, because court and commentators alike conceive of the criminal law as the body of law that administers punishment; and punishment, in turn, is conceived of in intention-sensitive terms. I argue that this understanding of the distinction between civil penalties and criminal punishments depends on a highly controversial proposition in moral theory — namely, that an agent’s intentions bear directly on what it is permissible for that agent to do, a view most closely associated with the doctrine of double effect. Therefore, legal theorists who are skeptical of granting intention this kind of significance owe us an alternative account of the distinctiveness of the criminal law. I sketch the broad outlines of just such an alternative account — one that focuses on the objective impact of legislation on a class of protected interests, regardless of the state’s motivations in enacting the legislation. In other words, even if the concept of punishment is unavoidably intention-sensitive, it does not follow that the boundaries of the criminal law are likewise intention-sensitive, because the boundaries of the criminal law may be drawn without reference to the concept of punishment. I conclude by illustrating the application of this view to a pair of well-known cases, and noting some of its ramifications.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.038 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".