EXPORT OF AGRICULTURAL PRODUCTS FROM THE STATES OF THE ASSOCIATION OF SOUTH-EASTERN ASIAN COUNTRIES (ASEAN)
Bibliographic record
Abstract
Background. ASEAN currently forms a solid economic bloc with a combined GDP of US $ 3 trillion, a relatively high share of agriculture in GDP (11.3% in 2010-2020) and an expanding agri-food market based on changing conditions. global and regional trade patterns. Purpose. It consists in analyzing and identifying the problems of agri-food exports of the states that are members of the Association of Southeast Asian Nations (ASEAN). Materials and methods. The research is based on the Declaration on the Establishment of ASEAN (Signed in Bangkok on August 08, 1967), statistics from the Food and Agriculture Organization of the United Nations, ASEAN, and the World Trade Center. In this work, dialectical, systemic, logical research methods were used. Results. As a result of the analysis carried out in the article on agri-food exports by product, it was revealed that animal, vegetable fats and oils are the most important agricultural products of the ASEAN countries, providing a quarter of regional and more than a third of world exports. Conclusion. The agro-export policy of the ASEAN countries is largely fragmented. This is mainly due to the internal differences between the states that make up this bloc.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".