1 The Global Food Crisis and What Has Capitalism to Do With It?1
Bibliographic record
Abstract
spoke publically of a silent tsunami ”that threatens to plunge more than 100 million people on every continent into hunger....This is the new face of hunger—the millions of people who were not in the urgent hunger category six months ago but now are. ” The price of staple grains, rice and maize which are the main sustenance of poor people, accounting for 63 percent of caloric intake in low income Asian countries, half of all calories in sub-Saharan Africa and only somewhat less in Latin America. In a large number of these poor countries, where people subsist on less than 2,200 calories a day, food imports account for a large part of their food and so higher prices and less food aid means starvation. While in the U.S. shoppers noticed the increases Americans in 2005 spent less than a seventh of their incomes on food (down from a quarter of average income in the 1960s, including food eaten out), this was not so terrible. In poor countries however food accounts for over half of the consumer price index and in places like Bangladesh and Nigeria, over two-thirds. In the first part of 2008 the president of the World Bank, Robert Zoellick warned that there were at least 33 countries which were at risk of social unrest due to rising food prices since there was no margin for survival as prices rose for so many of the 1 This paper is based on a talk given at the conference on “The Global Food Crisis ” at the
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".