Türkiye’s peach and nectarine exports: An empirical analysis with normalized revealed comparative advantage (NRCA) indexes
Bibliographic record
Abstract
The objective of this study was to examine the competitiveness of Türkiye in peach and nectarine exports through the use of normalized revealed comparative advantage (NRCA) indexes, encompassing cross-product group, cross-country, and cross-period comparisons. For this purpose, calculations were made based on HS-6 coded peach and nectarine, apricot, cherry, sour cherry and plum foreign trade data of Türkiye, Spain, the USA, Chile, Italy and Greece for 2001-2023. The study indicates that Türkiye's comparative advantage in peaches and nectarines increased significantly after 2016. The cross-product group comparison indicates that Türkiye has a competitive advantage in peaches and nectarines relative to other stone fruit exports, including apricots, cherries, and plums. However, especially in recent years, Türkiye has been at a comparative disadvantage in peach and nectarine exports relative to cherries. Regarding cross-country comparison, Türkiye has a comparative advantage over other major peach and nectarine exporters (Italy, Chile, USA and Greece). On the other hand, Türkiye has a comparative disadvantage compared to Spain, the world's largest exporter of peaches and nectarines. In terms of periodic comparison results, Türkiye has increased its competitiveness during the analysis period compared to previous years. To maintain and stabilize competitiveness, more exports are needed to different markets, particularly Canada, Mexico, Switzerland, the United Kingdom, Belgium, Germany and Saudi Arabia.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".