H2O for All? Examining the Potential of Public-Public Partnerships in the Water Sector
Bibliographic record
Abstract
Public-private partnerships (PPPs) in the water sector involve a private company carrying out the act of water provision while the state retains ownership of the service’s assets, an approach taken by various countries of the global South in the early 1990s. Recently, however, there has been a return to the public sector for water service in some areas in the form of public-public partnerships (PuPs) which create links across levels of government and between government and other public bodies. Interest in PuPs has been stimulated by an observed failure of adequate water service provision by PPPs (Bakker 2003; Hemson et al. 2006; Swyngedouw 2004). This presentation aims to first present a new, textured typology of the different types of PuPs employed in the global South. The PuP typology has been created by surveying academic, government, business, union and non-governmental organization literature. Second, the positive and negative aspects of each partnership configuration will be examined, particularly in terms of how effective each is at delivering water to marginalized communities. My analysis treats water as a “public” or “social” good, something that is essential to human health and well-being. The rationale behind this study is to isolate what types of PuPs would be beneficial to citizens in the global South and ensure that water is treated as a public good.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".