Bibliographic record
Abstract
Trade is an “engine of growth”; hence, foreign trade plays a pivotal role in growth and development of developing countries which enhances competitiveness, expands business opportunities for local companies, removes unnecessary barriers and makes it easier for them to export and import. China and India are the two emerging countries in the world whose are leading players in pharmaceutical sector. Both countries have benefited from opening up of international trade and trade relations, through initiating liberalization of policies. In last two decades, India has witnessed significant changes in trade and its policy, which resulted in the rapid growth of pharmaceutical sector. The global economy has been virtually dominated by the Chinese trade in nearly all manufacturing sectors particularly increasing its presence in bulk drugs which are required for the manufacturing of several essential drugs. With this back drop, the present paper is an attempt to analyze the trends of exports and imports in pharmaceutical sector of India and China with rest of the world. The study used secondary data which has been drawn from different websites of RBI, WTO, research papers and reports. For analyzing the data appropriate statistical tools have been used and result shows that there is a growth of export and import in pharmaceutical sector of both the countries but China has higher value in trade in comparison of India.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| 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) | 0.002 | 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".