Economic Cooperation And Integration Among BIMSTEC: A Study On Organic Agricultural Products
Bibliographic record
Abstract
The study on the economic benefits of India BIMSTEC sub-regional trade alignments for Indian organic products focuses on regional alliances among south and southeast Asia. This sub-regional trading bloc is most important for Indian policymakers to enhance economic cooperation through trade on organic Agri products among these regions. This research wants to highlight major tradeable organic Agri Products and identify potentiality in trade among these regions. Study also identifies the possible scope of trade and future welfare creation among BIMSTEC members through the formation of regional trade agreement (RTA) particularly, based on the organic Agri products basket. In this research, researcher restricts the scope of organic Agri products, grown under system of agriculture without the use of chemical fertilizers and pesticides. Researchers identify a list of 23 major organic Agri products under Harmonized System (HS) of classification under 4-digit codes for preliminary analysis of trade potentiality among these regions. This research has been established based on different econometric parameters commonly used for analyzing trade-related data. Basically, WITS (World Bank) data has been used for our analysis purposes. For identifying of intra-industry trade (IIT) researchers use the GrubelLloyd index and, for comparative evaluation of import effects in various forms of trade alignment namely, bi-lateral and sub-regional alignment, particularly in BIMSTEC countries researcher uses AI based Gravity model on trade
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".