The Intersection of Ultra-Processed Foods, Neuropsychiatric Disorders, and Neurolaw: Implications for Criminal Justice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Over the last decade there has been increasing interest in the links between the consumption of ultra-processed foods and various neuropsychiatric disorders, aggression, and antisocial behavior. Neurolaw is an interdisciplinary field that seeks to translate the rapid and voluminous advances in brain science into legal decisions and policy. An enhanced understanding of biophysiological mechanisms by which ultra-processed foods influence brain and behavior allows for a historical reexamination of one of forensic neuropsychiatry’s most famous cases—The People v. White and its associated ‘Twinkie Defense’. Here in this Viewpoint article, we pair original court transcripts with emergent research in neurolaw, including nutritional neuroscience, microbiome sciences (legalome), pre-clinical mechanistic research, and clinical intervention trials. Advances in neuroscience, and related fields such as the microbiome, are challenging basic assumptions in the criminal justice system, including notions of universal free will. Recent dismissals of criminal charges related to auto-brewery syndrome demonstrate that courts are open to advances at the intersection of neuromicrobiology and nutritional neuroscience, including those that relate to criminal intent and diminished capacity. As such, it is our contention that experts in the neurosciences will play an increasing role in shaping research that underpins 21st-century courtroom discourse, policy, and decision-making.
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.
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) | 0.000 | 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 it