A Macroeconomic Research on the Development of Agricultural Economy in the Kurdistan Region within the Scope of 2030-2050 Sustainable Development Goals (SDGs)
Bibliographic record
Abstract
This study will discuss the development of the Kurdistan Region in terms of agriculture and agri-food economy. The emergence of climate change due to global warming has caused concern about worldwide famine. Research by macroeconomists and agricultural economists shows that world resources are becoming increasingly scarce. With the Kurdistan Regional Government's (KRG) decision, there seems to be more focus on agricultural activities in the Kurdistan Region. Stable and sustainable agricultural policies to be implemented following the 2030-2050 Sustainable Development Goals (SDG's) will ensure that the Kurdistan Region It will enable it to be among the top 10 agricultural countries after the US, Canada, China, India, Australia, France, and Russia. Stable agricultural policies to be implemented in the Kurdistan Region, which has fertile lands, will also enable Gross Domestic Product (GDP) and Gross National Product (GNP) to rise and foreign investors to make significant investments in agriculture in the Kurdistan Region within the scope of Foreign Direct Investment (FDI). Studies by macroeconomists and agricultural economists show that oil currently accounts for a significant portion of the Kurdistan Region's GDP. However, the recent focus on agricultural production in the Kurdistan Region is projected to account for approximately 60.57% of Gross Domestic Product (GDP) by 2030 and approximately 72.10% by 2050.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".