Role of Geopolitical Factors in Determining India's Trade Potentials and Directions: A Gravity Model Analysis
Bibliographic record
Abstract
The paper intends to ascertain the geopolitical factors influencing India's trade besides finding out India's export potential using the gravity model estimates. The study covers seventy nine countries and augments the gravity model of trade by incorporating two major geopolitical variables, namely, India's hydrocarbon imports (HCim) as explained through the impact of speculative rise of oil prices, and, Free Trade Agreement (FTA) both of which are dummies in nature. The study reveals that India has huge export potential to countries including Cape Verde (ECOWAS), Luxembourg (EU) and Lao PDR (ASEAN). Moreover, India';s export potential to other countries like Canada, Chile, Indonesia, Korea, Kuwait, Mexico, Russia and Venezuela is also moderately high and a Free Trade Agreement between India and these countries could possible help in harnessing the estimated potential. India also needs to diversify its trade portfolio with Singapore, China and UAE with whom the actual trade supersedes the estimated potential. A strong bilateral linkage between India and OPEC countries is imminent as India's energy requirement is deemed to multiply in days to come.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".