Bibliographic record
Abstract
Being competitive in the international agri-food trade is an important aim of every country. It should be noted that this term has neither a commonly accepted definition nor a synthetized index to quantify it. The most commonly used indices in the international literature are the Balassa index and its modified versions (revealed trade advantage, revealed competitiveness, normalized revealed comparative advantage, and revealed symmetric comparative advantage) and different export and/or import-related indices (e.g., the Grubel–Lloyd index or the trade balance index). Based on a systematic review of the literature, these measurements were identified along with the major factors suggested for higher agri-food trade competitiveness. It seems that supportive legislation and/or (trade) policy is the most crucial factor, followed by higher value-added/more sophisticated goods, and high, efficient, and profitable production. Although the EU and its member states were overrepresented in the analyzed literature, the candidate countries, as well as other important trading partners of the EU, e.g., Canada, China, or the ASEAN countries, were also analyzed. Thus, some of these findings may be generalized.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".