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 A.
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 | 0.017 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.030 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.017 | 0.021 |
| Insufficient payload (model declined to judge) | 0.011 | 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; 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".