Water And Sanitation Services. Public Policy And Management
Bibliographic record
Abstract
Substantially reducing the number of human beings who lack access to clean water and safe sanitation is one of the key Millennium Development Goals. This book argues and demonstrates that this can only be achieved by a better integration of the technical and social science approaches in the search for improved organization and delivery of these essential services. It presents a historical analysis of the development of water and sanitation services in both developed and developing countries, which provides valuable lessons for overcoming the obstacles facing the universalization of these services. Among the key lessons emerging from the historical analysis are the organizational and institutional diversity characterizing the development of water and sanitation internationally, and the central role played by the public sector, particularly local authorities, in such development. It also explores the historical role played by cooperatives and other non-profit institutions in reaching rural and peri-urban areas, as well as the emergence of new forms of organization and provision, particularly in poor countries, where aid and development agencies have been promoting the self-organization of water systems by local communities. The book provides a critical exploration of these different institutional options, including the interaction between the public and private sectors, and the irreplaceable role of public funding as a condition for success. The book is divided into two parts: the first reviews theoretical and conceptual issues such as the political economy of water services, financing, the interfaces between water and sanitation services and public health, and the systemic conditions that influence the provision of these services, including the diversity of organizational and institutional options characterizing the governance and management of water and sanitation services. The second section presents a number of country or regional case studies, each one chosen to highlight a particular problem, approach or strategy. These case studies are drawn from Africa, the Americas, Asia and Europe, covering a wide range of socio-economic and political contexts. The book will be of great interest to advanced students, researchers, professionals and NGOs in many disciplines, including public policy and planning, environmental sciences, environmental sociology, history of technology, civil and environmental engineering, public health and development studies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.064 | 0.030 |
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".