Bibliographic record
Abstract
Multilateral organizations and research institutions are increasingly calling for transformation of the industrial food system due to its negative health impacts, its contribution to climate change and the fact that the system fails to provide adequate food to more than 800 million people. A foremost rationale given for food system change is the so-called obesity crisis. This commentary draws from critical weight studies and ecological public health discourses to argue that it is unnecessary to connect the crises of the food system with a rise in overweight and obesity. This approach contributes to fat stigma and further marginalizes a group of people who already suffer from stigmatization. A more inclusive rationale for food system change can be found in a concept articulated by the Canadian Public Health Association termed the ‘ecological determinants of health.’ These are features of the biosphere such as water, air, food and soil systems that support life on earth and human health. The current industrial food system threatens the ecological determinants of health by contributing substantially to climate change and environmental degradation. A shift in discourse in food policy and practice to focus on the ecological health impacts of the food system is more inclusive and promotes the well-being of all.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.044 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".