Direction of Trade and Destination Patterns of Indian Soybean Exports: An Analysis
Bibliographic record
Abstract
This study examined the direction of trade and changing patterns of Indian soybean exports over a 15-year period, from 2008 to 2022. Markov chain analysis and percentage analysis were employed to analyze the export trends. During this period, India's soybean exports underwent significant growth, with the United States and Canada emerging as major importers. The Markov chain analysis revealed that the United States retained 88.27 per cent of its export share, while Canada retained 71.39 per cent. In contrast, the UAE showed zero per cent retention, indicating an unstable market, and re-exported 100 per cent of its imports from India to Canada. The study also identified France, Sri Lanka, and the United Kingdom as potential markets for Indian soybean exports. Notably, India's soybean exports peaked at 292,491 metric tonnes in 2017, valued at 166,258.07 thousand USD. The findings suggested that India should have focused on strengthening trade relationships with stable markets while exploring ways to stabilize exports to volatile markets like the UAE to reduce dependency on volatile destinations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".