Solving climate change requires changing our food systems
Bibliographic record
Abstract
Abstract Humanity is facing an important existential threat—irreversible climate change caused by human activity. Until recently, most of the proposals to address climate change have downplayed or ignored the adverse impact of food systems, especially intensive animal agriculture. This is in spite of the fact that up to a third of global greenhouse gas production to date can be attributed to animal agriculture. Recent developments at COP28 have signaled that the tide is turning, however, and that food systems are becoming part of global discussions on climate change solutions. The pressing nature of irreversible climate change requires rethinking our food systems. To solve the climate change crisis, we propose transitioning to a predominantly plant-based diet, and phasing out intensive animal agriculture as diets shift, without increasing pastoral farming. We suggest that such transformations in global food systems can be accomplished largely through education and large-scale public information campaigns, removal of subsidies, taxation to account for externalized costs of animal agriculture, improved labelling of products, and various investment/divestment drivers. Better metrics and industry benchmarks involving food and agriculture-specific performance indicators that reflect food system sustainability will be important. Increased global awareness of these issues and a change in mindset (which will drive political will) also are needed. Our current trajectory is untenable, and we must begin to turn the ship now towards sustainable food systems and diets.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".