Comparative Analysis of Turkey's Competitive Position in Agricultural Exports with Selected Competitor Countries
Bibliographic record
Abstract
This study measures Turkey's competitive strength in agricultural exports between 2014 and 2023 using Revealed Comparative Advantage (RCA) and Symmetric RCA (RSCA) indices and compares the findings with structurally similar economies such as Brazil, the United States, Canada, China, Italy, Spain, and the Netherlands. The data show that Turkey's agricultural exports rose from US$19.54 billion in 2014 to US$30.26 billion in 2023, with a period average of US$21.47 billion. In the agricultural sector, RCA values ranged between 1.56 and 1.85, averaging 1.67, while RSCA remained between 0.22 and 0.30, averaging 0.25, confirming Turkey's permanent comparative advantage. Comparatively, Brazil's average RCA and RSCA values are quite high at 5.87 and 0.71, respectively, while the US and Canada's RCA is 1.64 and 1.83, and their RSCA is 0.24 and 0.29. In Italy and Spain, the RCA ranges from 0.6 to 1.1 and the RSCA is mostly negative, while in the Netherlands, the RCA ranges from 0.95 to 1.10 and the RSCA varies between -0.02 and 0.05. As a result, Turkey has a stronger agricultural competitive position compared to similar European economies, but a medium-level position compared to producers with high RCA, such as Brazil and Ukraine. The fact that the RCA in Ukraine ranges from 4.57 to 8.44 and the RSCA from 0.64 to 0.79, and that the RSCA values in Turkey rose from 0.22 to 0.26 in the 2019–2023 period, highlights the importance of policies focused on higher added value and productivity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".