Enhancing Economic Integration in South Asia: Issues and Prospects for Closer Monetary Cooperation
Bibliographic record
Abstract
Though SAARC has the stated goal of an economic union and a common currency, after a quarter of a century, these remain distant goals as political tensions between India and Pakistan have hindered any real progress on a regional scale. Barriers to trade and factor mobility are high in the region as a whole, though considerable liberalisation has been achieved through various bilateral agreements involving India and some of its neighbours. The conventional economic conditions for a common currency are also currently absent as patterns of shocks are non-synchronised, trade links are weak and factor mobility much constrained. Deeper intraregional economic integration requires much more comprehensive trade and investment liberalisation among member nations. While the political conditions for a single currency are unlikely to emerge in the foreseeable future, steps towards closer monetary cooperation through a South Asian Monetary System — building on the existing SAARCFINANCE network — may provide an institutional framework for enhancing regional integration. However, such cooperation will have to be conceived as a component of a sustainable transitional strategy which commits to a serious programme of deeper trade liberalisation to facilitate greater integration with the rest of the world, and most importantly, with East Asia.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".