Dispute Settlement in Indian FTAs’: Shaping the Future with Lessons from the Past
Bibliographic record
Abstract
India is no stranger to free trade agreements (FTAs), having notified seventeen of them as ‘in force’ to the WTO. In the last six months, India has concluded two new FTAs and is also actively engaged in the process of negotiating major ones with its key trading partners such as the UK, EU and Canada. With the WTO’s Appellate Body still non-functional, the spotlight is now on the dispute settlement mechanisms under the FTAs and their potential to be a viable alternative to the multilateral mechanism. Hence, an analysis of the select practices in the existing Indian FTAs is not just timely but also critical. This short article attempts to evaluate five specific elements pertaining to dispute settlement in Indian FTAs, namely the scope, the choice of forum, the structure of the dispute resolution mechanism, the process of the appointment of arbitrators/panellists and the case of non-implementation/retaliation. Through this examination, the article attempts to identify and propose specific elements for any future dispute settlement framework in India’s upcoming FTAs. dispute settlement mechanism, Indian FTAs, WTO, free trade agreements, choice of forum, appointment of arbitrators, arbitration, negotiations, panel
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.020 | 0.034 |
| Scholarly communication | 0.037 | 0.019 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 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".