برآورد تاثیر متقابل حمایت مرزی و داخلی در بخش کشاورزی ایران
Bibliographic record
Abstract
One of the unique characteristic of agricultural agreement is to order the countries to accept the commitments in three different areas such as domestic support, market access and export subsidy. Although the main goal of this type of classification is the reduction of distortion interventions of trade, but indicates that the agricultural agreement has special attention to political tools against the trade effects of agricultural policies. Whereas it is expected that the attention to the trade effects of policies must has more priority. This subject has been taken into consideration by researches formerly, yet in this paper a method is presented for the first time, to measure the amount of reciprocal effect in two areas, market access and domestic support. The result of this essay shows that in Iran like countries such as U.S and Canada the government supports some of agricultural producers with applying border and domestic support at the same time. Disregarding to this stimulatingly causes under estimation of domestic price support about %161. This result confirmed the conclusions of Anania (1997), Gurter and Ingco (2002) that are based upon the impressionability of domestic support from border policies and the necessity of redefine of amber box. But it is against the conclusion of Anania (1997) that is based upon over estimation of aggregate measurement support because of stimulating of these policies. JEL: Q18, Q17, F13
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.072 | 0.034 |
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".