Bibliographic record
Abstract
In a recent visit to India the Chinese president, Hu Jintao, proposed closer economic relations between China and India, possibly a India–China free trade area (FTA). These two economies have been experiencing rapid growth during the last couple of decades and in recent years trade between these two nations has grown spectacularly. This article analyzes the implications of a possible India–China FTA on trade flows, real output and investment both at the aggregate and industry levels in India, China, the rest of Asia, the North American and European economies using a multi-sector, multi-region dynamic computable gen-eral equilibrium (CGE) model. Our simulation results suggest that the overall economic gains to India and China would be modest. The distribution of the economic gains, however, depends on the speed of elimination of the bilateral tariffs. China gains more if the tariffs are eliminated immediately, whereas India gains more from gradual liberalization. India’s exports to China could expand by almost 57 per cent, while imports from China could increase by over 240 per cent implying an increased bilateral trade deficit. Output in each sector in India would increase. Sectors such as clothing, leather, textiles and motor vehicles and parts would gain the most in 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".