Criminal Law and Neuroscience: Theory and Practice in the Italian Perspective
Bibliographic record
Abstract
The debate on the impact on criminal justice of the empirical evidence offered by techniques of brain exploration and behavioral genetics shows no sign of diminishing, fed by literature now boundless and by case law in constant growth. In the Italian system, the impact of neuroscience at trial is still rather limited and substantially confined to its sedes naturalis, that is to say, the insanity defense. Even in this area, however, there is a very cautious, if not sometimes distrustful, attitude on the part of the courts, still doubtful about the epistemological reliability of neuroscientific evidence. The interdisciplinary dialogue is called upon to help overcome uncertainties and resistance, to avoid the underestimation of data endowed – albeit in a complementary and integrative function – with an increasingly objective value. Summary: 1. Foreword. - 2. Neuroscience: an outline. - Neuroscience and criminal law in the light of the radical-revolutionary model. - 4. Neuroscience and criminal law in the light of the moderate-compatibilist model. - 5. An overview of the use of “neuroscientific evidence” in practice - 6. Neuroscience and the evaluation of criminal capacity: a first assessment. - 7. The (still) limited impact of neuroscience in the Italian criminal trial. Diagnosis and prognosis. – 8. Conclusions.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.045 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".