Beyond Auto-Brewery: Why Dysbiosis and the Legalome Matter to Forensic and Legal Psychology
Bibliographic record
Abstract
International studies have linked the consumption of ultra-processed foods with a variety of non-communicable diseases. Included in this growing body of research is evidence linking ultra-processed foods to mental disorders, aggression, and antisocial behavior. Although the idea that dietary patterns and various nutrients or additives can influence brain and behavior has a long history in criminology, in the absence of plausible mechanisms and convincing intervention trials, the topic was mostly excluded from mainstream discourse. The emergence of research across nutritional neuroscience and nutritional psychology/psychiatry, combined with mechanistic bench science, and human intervention trials, has provided support to epidemiological findings, and legitimacy to the concept of nutritional criminology. Among the emergent research, microbiome sciences have illuminated mechanistic pathways linking various socioeconomic and environmental factors, including the consumption of ultra-processed foods, with aggression and antisocial behavior. Here in this review, we examine this burgeoning research, including that related to ultra-processed food addiction, and explore its relevance across the criminal justice spectrum—from prevention to intervention—and in courtroom considerations of diminished capacity. We use auto-brewery syndrome as an example of intersecting diet and gut microbiome science that has been used to refute mens rea in criminal charges. The legalome—microbiome and omics science applied in forensic and legal psychology—appears set to emerge as an important consideration in matters of criminology, law, and justice.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".