What commodities and countries impact inequality in the global food system?
Bibliographic record
Abstract
The global distribution of food production is unequal relative to the distribution of human populations. International trade can increase or decrease inequality in food availability, but little is known about how specific countries and commodities contribute to this redistribution. We present a method based on the Gini coefficient for evaluating the contributions of country and commodity specific trade to inequality in the global food system. We applied the method to global food production and trade data for the years 1986–2011 to identify the specific countries and commodities that contribute to increasing and decreasing inequality in global food availability relative to food production. Overall, international trade reduced inequality in food availability by 25%–33% relative to the distribution of food production, depending on the year. Across all years, about 58% of the total trade links acted to reduce inequality with ∼4% of the links providing 95% of the reduction in inequality. Exports from United States of America, Malaysia, Argentina, and Canada are particularly important in decreasing inequality. Specific commodities that reduce inequality when traded include cereals and vegetables. Some trade connections contribute to increasing inequality, but this effect is mostly concentrated within a small number of commodities including fruits, stimulants, and nuts. In terms of specific countries, exports from Slovenia, Oman, Singapore, and Germany act to increase overall inequality. Collectively, our analysis and results represent an opportunity for building an enhanced understanding of global-scale patterns in food availability.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".