Comparative Advantage and Market Distortions: A Policy Analysis Matrix for Iraqi Wheat Crop Production
Bibliographic record
Abstract
Wheat is the most crop have been subsidized by the government in Iraq, through subsidizing the input of the production (seed, pesticide, and machines), as well as, subsidize the output of the production through purchasing it from the producers at a high price compared to the world market price. The study aims to assess the competitive advantage of wheat production in Iraq through some of the measures derived from the policy analysis matrix. This study according to secondary data has published by Iraqi Ministry of Planning/Central Organization of Statistics and Information Technology 2018, for wheat production costs of cultivation season in Iraqi provinces 2017. The results of the study indicated that the coefficients measures show, there is a government subsidy for wheat output and that means, producers receive prices higher than international prices with the existence of this policy. While the comparative advantage indicators showed, the wheat crop in Iraq was achieved private profits due to government intervention in the inputs and outputs of production, nor competitive advantage in social prices. Where the policy reflection indexes/market distortions analysis shows, that the government policy for wheat production 2017 subsidized the producers on the consumer cost, where the local market price for wheat is higher than the price of wheat in the world market.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".