Solving climate change requires changing our food systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it