FOCUS REVIEW Food, Global Environmental Change and Health: EcoHealth to the Rescue?
Bibliographic record
Abstract
Everything is changing, and changing rapidly, except how we think. What we eat brings issues of health and global environmental change to the table in ways that are urgent, global, and full of scientific uncertainty. Eating is the most intimate relationship we have with the environment, when various parts of plants and animals are integrated into our bodies. Our eating habits link human nutrition (and all the health issues associated with that) and infectious foodborne diseases to agricultural practices, land use, global trade, poverty, economic inequity and climate change. Official estimates of the incidence of endemic foodborne diseases (as differentiated from outbreaks) from both Canada and the United States show that there were increasing trends from the 1970s to the late 1980s and 1990s. This was the period when Western industrialized countries saw the emergence of new variant Creutzfeldt-Jakob Disease associated with bovine spongiform encephalopathy, serious diseases caused by shiga-toxin-producing E. coli, the pandemic of Salmonella enteritidis and the recognition of listeriosis as a foodborne illness. While one can pinpoint specific causal pathways for each of the diseases, they all reflect more general systemic and cultural changes, including population growth and mobility, a huge shift in agriculture to economies of scale and mass distribution, land use changes including manure production by large livestock enterprises, an expectation of low food costs at the grocery store, relatively low oil costs, better detection methods and a more alert public (1,2). The increases of the 1980s might be said to have culminated, at least in the public eye, in the 1993 deaths *To whom correspondence should be addressed:
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.005 |
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".