Bibliographic record
Abstract
The central debate in the field of neurolaw has focused on two claims.Joshua Greene and Jonathan Cohen argue that we do not have free will and that advances in neuroscience will eventually lead us to stop blaming people for their actions.Stephen Morse, by contrast, argues that we have free will and that the kind of advances Greene and Cohen envision will not and should not affect the law.I argue that neither side has persuasively made the case for or against a revolution in the way the law treats responsibility.There will, however, be a neurolaw revolution of a different sort.It will not necessarily arise from radical changes in our beliefs about criminal responsibility but from a wave of new brain technologies that will change society and the law in many ways, three of which I describe here: First, as new methods of brain imaging improve our ability to measure distress, the law will ease limitations on recoveries for emotional injuries.Second, as neuroimaging gives us better methods of inferring people's thoughts, we will have more laws to protect thought privacy but less actual thought privacy.Finally, improvements in artificial intelligence will systematically change how law is written and interpreted
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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; both teacher heads agree on what is shown here.
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".