Contested Sustainabilities: The Post-carbon Future of Agri-food, Rural Development and Sustainable Place-making
Bibliographic record
Abstract
First paragraphs: Humans eat a lot of meat! According to the Food and Agriculture Organization of the United Nations (FAO), the annual consumption of meat globally in 2013 was 106 lbs. (48 kg) per capita, up from 56 lbs. (25 kg) in 1961 (FAO, 2018). This amount is projected to increase by between 75% and 145% by 2050 (Godfray et al., 2018), due to the strong correlation between increasing per-capita gross domestic product (GDP) and increasing per-capita meat consumption (Tilman and Clark, 2014). And to provide this meat (along with other animal products), there are about 30 billion livestock animals in the world at any given time—four times the number of humans; over 160 billion livestock are slaughtered annually, half of these poultry (FAO, 2018). No wonder that meat’s impact on our planet and our lives is so large. The implied question permeating Wilson Warren’s book is “Why do we eat so much meat?” The title suggests one answer—the belief that Meat Makes People Powerful—and the text makes clear that this is in terms of health, culture, and economics. The final chapters ask a further question—How can we stop eating so much meat? They describe the major role that meat is playing in anthropogenic climate change and environmental pollution in general, as well as in the current global noncommunicable disease pandemic. They also discuss the overwhelmingly negative effects of meat consumption on animal welfare and on social equity. . . .
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 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".