Nutritional Criminology: Why the Emerging Research on Ultra-Processed Food Matters to Health and Justice
Bibliographic record
Abstract
There is mounting concern over the potential harms associated with ultra-processed foods, including poor mental health and antisocial behavior. Cutting-edge research provides an enhanced understanding of biophysiological mechanisms, including microbiome pathways, and invites a historical reexamination of earlier work that investigated the relationship between nutrition and criminal behavior. Here, in this perspective article, we explore how this emergent research casts new light and greater significance on previous key observations. Despite expanding interest in the field dubbed 'nutritional psychiatry', there has been relatively little attention paid to its relevancy within criminology and the criminal justice system. Since public health practitioners, allied mental health professionals, and policymakers play key roles throughout criminal justice systems, a holistic perspective on both historical and emergent research is critical. While there are many questions to be resolved, the available evidence suggests that nutrition might be an underappreciated factor in prevention and treatment along the criminal justice spectrum. The intersection of nutrition and biopsychosocial health requires transdisciplinary discussions of power structures, industry influence, and marketing issues associated with widespread food and social inequalities. Some of these discussions are already occurring under the banner of 'food crime'. Given the vast societal implications, it is our contention that the subject of nutrition in the multidisciplinary field of criminology-referred to here as nutritional criminology-deserves increased scrutiny. Through combining historical findings and cutting-edge research, we aim to increase awareness of this topic among the broad readership of the journal, with the hopes of generating new hypotheses and collaborations.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| 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".