Comparative Analysis of Export Competitiveness Specialization Levels of Türkiye and Leading Countries in the Cereal Sector
Bibliographic record
Abstract
The aim of this study is to determine the export competition specialization level of the cereal sector of Türkiye and the ten countries (USA, Germany, France, India, Canada, Brazil, Argentina, Ukraine, Australia and Russia) that have the largest share in cereal exports and to analyze them from a comparative perspective. In this direction, the export and import values of the said countries for the period 2013-2022 were taken from the WITS (World Integrated Trade Solution) database. Analyzes, SITC Rev. it was made using the Revealed Comparative Advantages (RCA) method for 3 cereal sub-product groups in the product group “04- Cereals, cereal products” belonging to 3 groups. According to the Net Export Index results, it has been detected that Germany, India, Brazil, Türkiye, Ukraine (except 0481), Russia, Argentina and Australia (except 0471) specialize in the export of all sub-product groups. However, it is concluded that the USA could not specialize in the export of any of the aforementioned sub-product groups. In addition, it has been determined that France and Canada only specialize in the export of the 0472 coded product group. Balassa Index results show that these countries have a competitive disadvantage in all cereal sub-product groups.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".