Neurolaw: potential applications of fMRI in courts
Bibliographic record
Abstract
Functional magnetic resonance imaging (fMRI) is a neuroimaging technique used to study cognitive functions. Despite fMRI having successfully been used to identify many cognitive capabilities, recent research has not found any successful submissions of fMRI evidence in criminal courts in Australia, Canada, England and Wales. Neurolaw is an interdisciplinary area involving neuroscience, law, and philosophy. Publications in neurolaw and research investigating the applications of fMRI in the legal context are increasing. One probable explanation for the lack of admissions of fMRI in the courts is that this is due to the numerous limitations of fMRI. However, many potential applications of fMRI have been recommended. These include lie detection, testing of guilty knowledge, and mind reading. After evaluating the medical uses of fMRI, analysing court cases involving functional neuroimaging evidence and considering the history of imaging evdence,my thesis identifies several potential areas where fMRI might be applicable within the legal context. My study also suggests a hypothetical case that supports a conceptual claim that fMRI migh have potential to be useful in court. Moreover, the hypothetical case supplies a few directions for further study.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".