Actions speak louder than images: the use of neuroscientific evidence in criminal cases
Bibliographic record
Abstract
This invited commentary for Journal of Law & the Biosciences considers four empirical studies previously published in the journal of the reception of neuroscientific evidence in criminal cases in the United States, Canada, England and Wales, and the Netherlands. There are conceded methodological problems with all, but the data are nonetheless instructive and suggestive. The thesis of the comment is that the courts are committing the same errors that have bedeviled the reception of psychiatric and psychological evidence. There is insufficient caution about the state of the science, and more importantly, there is insufficient understanding of the relevance of the neuroscientific evidence to the precise legal question being addressed. These studies demonstrate yet again that in virtually all cases, actions speak louder than images and that when the behavioral evidence is unclear, the neuroscientific evidence is scarcely helpful in resolving the legal issue.
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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 | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".