Unravelling the Food-Health Nexus to Build Healthier Food Systems
Bibliographic record
Abstract
The urgent call to transform global food systems is well founded on the need to reduce the effects of food systems on human health, environment, peoples' rights, and creation of a just society. Unhealthy diets contribute significantly to the global disease burden and pose huge risks to morbidity and mortality. Efforts to transform diets are highly dependent on transformation of the food system. All countries are now affected by the various forms of malnutrition - undernutrition, overweight and obesity, micronutrient deficiencies - with progress often too slow and in some cases going into reverse. Concomitantly, the number of food insecure is increasing, and the prevalence of non-communicable disease is high. IPES-Food, in collaboration with the Global Alliance for the Future of Food, undertook a review of the scientific evidence covering a whole range of global health impacts associated with food systems. The review examined how food and farming systems affect human health, explored why the negative impacts are systematically reproduced and why we fail to prioritize them politically, and how we can build healthier food systems for all. Five categories of health impacts were examined: (i) occupational hazards; (ii) environmental contamination; (iii) contaminated, unsafe, and altered foods; (iv) unhealthy dietary patterns, and (v) food insecurity. The study confirmed that food systems affect health through multiple, interconnected pathways, generating severe human and economic costs. It also highlighted how prevailing power relations in the food system help to shape and sometimes obscure our understanding of the impacts. Five leverage points for building healthier food systems are recommended: (i) promotion of food systems thinking; (ii) reasserting scientific integrity and research as a public good; (iii) bringing the alternatives to light; (iv) adopting the precautionary principle, and (v) building integrated food policies under participatory governance.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".