Bibliographic record
Abstract
This paper is an attempt at the refutation of certain fallacies, which have gained a wide currency in legal and criminological thinking. These fallacies are the following. First, the mistaken interpretation of universal statements such as “Any person condemns murder” as the expression of a cross-cultural consensus about the blameworthiness of a certain type of behaviour; such statements, it is argued, are mere tautologies reflecting the cogency of our linguistic customs. Second, the erroneous belief that criminology can dogmatically account for the sum of the facts which appertain to its field of study, by means of a single, all-encompassing explanation; arguments are given to show that the fate of criminological studies is fragmentation. Third, it is argued that the criminal justice system should be conceived as an apparatus for social provocation rather than as institutionalized social reaction. Fourth, it is pointed out that we must draw an unambiguous distinction between the legal notion of a sentence and the intuitive notion of punishment; stressing this difference leads the author to compare briefly the main tenets of what he respectively calls dogmatic and sceptical criminology. Finally, the necessity to recognize as separate issues the justification and the allocation of criminal sanctions is proven and it is shown how the penal fascination with capital punishment is responsible for blurring the distinction between these issues.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.065 | 0.010 |
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".