Transforming Water Supply Regimes in India: Do Public-Private Partnerships Have a Role to Play?
Bibliographic record
Abstract
Public-private partnerships (PPP) are an important governance strategy that has recently emerged as a solution to enhance the access of marginalised residents to urban infrastructures. With the inception of neo-liberal economic reforms in India, in Indian cities too PPP has emerged as an innovative approach to expand coverage of water supply and sanitation infrastructures. However, there has been little study of the dynamics of partnership efforts in different urban contexts: What role do they play in transforming existing infrastructure regimes? Do reform strategies such as partnerships result in increased privatisation or do they make the governance of infrastructures more participative? Reviewing some of the recent literature on urban political analysis, this article develops the concept of water supply regime to describe the context of water provision in three metropolitan cities in India. To further our understanding of the role of PPP within regimes, this article sketches five cases of water supply and sanitation partnerships located within these three metropolitan cities. From these empirical studies, the article arrives at the conclusion that while PPP are always products of the regime-context they are inserted within, quite often strategic actors in the partnership use the PPP to further their interests by initiating a shift in the regime pathway. This leads us to conclude that PPPs do play a role in making water supply regimes more participative but that depends on the nature of the regime as well as the actions of partners.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".