“Ditch red meat and dairy, and don’t bother with local food”: The problem with universal dietary advice aiming to save the planet (and your health)
Bibliographic record
Abstract
In recent years there have been increasing calls for “global dietary transition” in order to save the planet and improve human health. One troubling development associated with this is the attempt to delineate in universal terms what constitutes a sustainable and healthy diet. This perspective takes issue with this development, and specifically refutes one increasingly popular dietary narrative which calls for people to avoid red meat and dairy, and which portrays the local food movement as a romantic distraction. In contrast, the paper provides evidence of a range of sustainability and health benefits associated with both local food systems and the agri-food system’s inclusion of ruminants (the suborder of mammals from which humans mostly derive red meat and dairy). Finally, the perspective calls for a pluralist and multi-scalar approach to the multifaceted challenges associated with food production.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.031 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 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".