Water Federalism, Tribunalization of Water Justice and Hydro-Politics: India’s Inter-State River Water Disputes Act at 65 Years
Bibliographic record
Abstract
India’s water federalism is at a crossroads. It is a unique two-tier system that has the constitutional and enabling provisions for water management and inter-state water dispute resolution as its base. These support the tribunal system that adjudicates inter-state river water disputes and administers water justice. More than six decades have elapsed since its establishment. At the same time, during this period, the per capita water availability has fallen drastically. India is now one of the world’s most water-stressed countries. Water disputes between States are becoming more animated and highly volatile. This article examines water federalism in India in terms of two questions: 1) Should water be transferred from the State List to the Concurrent List? 2) Should India persist with the tribunal system or replace it with the judicial process at the Supreme Court level? The first assumes importance as India persists with the river linking project. The second is relevant because the Inter-State River Water Disputes Act is almost 65 years old. In 2016, India’s Supreme Court re-wrote the law, and, more recently, the Union Government sought to revamp the Inter-State River Water Disputes Act through amendments. All these impel the need to re-look the idea of water federalism as it operates in India in its entirety.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| 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".