Agricultural Support in OECD-Reported Countries from 2000 to 2019
Bibliographic record
Abstract
Agricultural support is one of the main tools used by governments to achieve their domestic goals, especially since the food shortages during and immediately after World War II. However, specific agricultural support programs can affect agricultural production in various ways, and support programs can alter the allocation of natural resources domestically and abroad. In this study, we measured agricultural support in OECD-reported countries during the period 2000-2019 using Spearman´s correlation coefficient, time trend analysis and clustering procedures. Data from Organization for Economic Co-operation and Development (OECD) from 2000 to 2019 were employed, specifically the Producer Support Estimate (PSE) and Consumer Support Estimate (CSE). We compared the results of two agglomerative clustering methods and identified groups of similar countries on the basis of their consumer support and producer support estimates behavior during the period studied. Some countries, such as Switzerland, South Korea, Turkey and Canada, displayed specific support behavior, while other groups of countries shared similarities such as China, Indonesia and the Philippines; the European Union, Japan and Norway; and Brazil, South Africa and Chile. Policies implications are discussed and further research is recommended, including analyses of top-down geographical unities, crop-specific programs, and the effects of the COVID-19 pandemic on agricultural support worldwide, as more data becomes available.
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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 | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".