Agricultural Protectionism, Its Measurement and Turkey
Bibliographic record
Abstract
This study reviews conceptual framework of agricultural protectionism, relevant measurement issues, and changes in agricultural protectionism with time in selected countries based on the composition of supports. When measuring the levels of agricultural protection, OECD method, the most widespread one, was employed, and related criticisms were discussed. In order to determine levels of protection, 11 countries, which are thought to have a significant role in the world agricultural markets and/or in terms of protectionism, were selected. These countries were grouped as low, medium and high protection countries, based on their Nominal Assistance Coefficients. Further, differing applications and specific conditions of those countries were discussed. Producer Support Estimate Percentages, Nominal Assistance Coefficient and Nominal Protection Coefficient were used to analyze changes in the protection level of the countries. Nominal Assistance Coefficients are found to be as follows: 1, 04-1, 11 in low protection countries (Australia, Brazil, China), 1, 16-1, 43 in medium protection countries (United States of America, European Union, Canada, Russia, Turkey) and 2, 12-2, 76 in high protection countries (South Korea, Switzerland, Japan). Although share of decoupled payments in support compositions increases, share of market price supports causing price distortions is still high. Furthermore, it was also observed that importance of environmental issues is increasing in almost all countries. Based on nominal protection coefficient, it can be said that countries are protecting staple crops more. In this case, concerns of the countries on being self sufficient at least for these crops and decreasing their dependency on world markets are affecting the decisions of those countries. Hence, it can be concluded that agriculture will remain as the most controversial issue in free trade negotiations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".