Beyond nutrition and agriculture policy: collaborating for a food policy
Bibliographic record
Abstract
Global interest in food policy is emerging in parallel with mounting challenges to the food supply and the rising prevalence of diet-related chronic health conditions. Some of the foundational elements of food policies are agricultural practices, finite resources, as well as economic burdens associated with a growing and ageing population. At the intersection of these interests is the need for policy synchronisation and a better understanding of the dynamics within local, regional and national government decision-making that ultimately affect the wellness of the populous and the safety, quality, affordability and quantity of the food supply. Policies, synchronised or not, need to be implemented and, for the food industry, this has seen a myriad of approaches with respect to condensing complex nutritional information and health claims. These include front and/or back of pack labelling, traffic light systems, etc. but in general there is little uniformity at the more regional and global scales. This translation of the nutritional and health-beneficial messages accompanying specific products to the consumer will undoubtedly be an area of intense activity, and hopefully interaction with policy makers, as the food industry continues to become a more global industry.
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 | 0.097 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.031 | 0.028 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.041 | 0.029 |
| Insufficient payload (model declined to judge) | 0.031 | 0.007 |
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".