Bibliographic record
Abstract
When I was a child, my mother implored me to eat everything on my plate because people are starving in Africa. The non sequitur was apparent, even to me. The amount of food children in wealthy countries waste has little or no effect on the amount children in poorer nations have to eat. Industrial economies produce vast farm surpluses. In spite of these surpluses, many of the world's poor will go to bed hungry. Food shortages arise because of failures in distribution, not in production. This is the common wisdom many analysts accept.1 If this common wisdom is correct, agricultural biotechnology will affect industrial economies and peasant economies differently. In many industrial economies, for example, in the United States and in Europe, three chronic problems plague agriculture: glut, glut, and more glut. Even without the benefit of biotechnology, farm commodities flood markets and drive prices below costs?leading to trade wars as European, Australian, Canadian, and South American producers compete for buyers. Bailouts, payments for not growing crops, and export subsidies have been hallmarks of farm policy. Price is the clear indicator of plenty and scarcity. If goods are scarce relative to demand, prices rise; if they are plentiful, prices fall. Last year, prices paid to wheat farmers on the Great Plains fell to $2.06 per bushel?the same price as in 1866 in nominal terms, i.e., with no adjustment for inflation.2 Surpluses beset
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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 | 0.022 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.049 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.028 | 0.033 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".