Bibliographic record
Abstract
Since 2008, there has been an increasingly influential narrative that world crop production must (“sustainably”) double from current levels in order to feed over nine billion people by 2050 (FAO, 2009; Ray, Mueller, West, & Foley, 2013; Soil Association, 2010; Tilman, Balzer, Hill & Befort, 2011; UN, 2009), which has been enthusiastically embraced by large agro-input and agrifood corporations. Four prominent drivers feature in this doubling narrative: the magnitude of persistent hunger and malnourishment; further human population growth; expanded biofuel production; and expected dietary changes. At first glance, this conveys the appearance of a sober, objective assessment about the fact that there are many people hungry today, that there will soon be at least two billion more people, that more land is being devoted to biofuels given the limits to conventional fossil energy supplies, and that people with rising incomes will keep eating more animal products in line with past trends, what I have called the meatification of 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.
How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".