Neurolaw: Revisiting Huberty v. McDonald’s through the Lens of Nutritional Criminology and Food Crime
Bibliographic record
Abstract
Recent studies have illuminated the potential harms associated with ultra-processed foods, including poor mental health, aggression, and antisocial behavior. At the same time, the human gut microbiome has emerged as an important contributor to cognition and behavior, disrupting concepts of the biopsychosocial ‘self’ and raising questions related to free will. Since the microbiome is undeniably connected to dietary patterns and components, the topics of nutrition and microbes are of heightened interest to neuroscience and psychiatry. Research spanning epidemiology, mechanistic bench science, and human intervention trials has brought legitimacy to nutritional criminology and the idea that nutrition is of relevance to the criminal justice system. The individual and community-level relationships between nutrition and behavior are also salient to torts and the relatively new field of food crime—that which examines the vast harms, including grand-scale non-communicable diseases and behavioral outcomes, caused by the manufacturers, distributors, and marketers of ultra-processed food products. Here in this essay, we will synthesize various strands of research, reflecting this emergent science, using a notable case that straddled both neurolaw and food crime, Huberty v. McDonald’s (1987). It is our contention that the legalome—microbiome and omics science applied in neurolaw and forensics—will play an increasing role in 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.
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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.012 | 0.057 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.020 | 0.027 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".