Tarımsal Korumacılık, Korumacılığın Ölçümü ve Türkiye [Agricultural Protectionism, Its Measurement and Turkey]
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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 it