Beyond Auto-Brewery: Why Dysbiosis and the Legalome Matter to Forensic and Legal Psychology
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
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.
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