Evaluación de políticas públicas para la seguridad alimentaria en países con diferentes niveles de desarrollo
Bibliographic record
Abstract
The aim of this article is to analyze public policy for food security adopted in countries with different development levels. For this purpose, a review in available previous studies was made. We used the country income in order to establish the levels of development, according to the World Bank methodology. The high-income countries selected were United States of America, Australia, New Zealand and Canada; with upper middle-income, Brazil, South Africa and Malaysia; with lower-middle income, China, India and Ecuador; and, on low income cases, Ethiopia, Nigeria and Vietnam. The results show that income inequality between countries and within each nation are determinants of food (in)security. The main causes of food insecurity in high-income countries and upper middle are disparities related to ethnicity/race, gender, income and education. In countries with low and lower middle income, inefficiency in institutions, low technology employed in agriculture and, in turn, low productivity are the main determining factors for food insecurity. Compensatory policies, although no long-term effectiveness are important integrating factor of the population placed on the banks of consumption by historical factors. As for the problems brought by excessive food consumption, obesity begins as a problem among groups of socioeconomic status higher in low-income countries, but as the country’s income grows, the risk of obesity reach poorest population increases. Interventions should be undertaken in order to make healthy food more accessible to low-income population. To do this, it is necessary the combination of agricultural policy, pricing policies, regulatory actions and education on consumption.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".