Bibliographic record
Abstract
Peter Debaere of Darden Business School, University of Virginia reviews “Water Pricing Experiences and Innovations”, by Ariel Dinar, Victor Pochat, and Jose Albiac-Murillo. The Econlit abstract of this book begins: “Twenty-two papers examine water pricing experiences in various countries from 2000 to 2015. Papers discuss water pricing in Australia—unbundled politics, accounting, and water pricing; water pricing in Brazil—successes, failures, and new approaches; water pricing in Canada—recent developments; water pricing in Chile—decentralization and market reforms; water pricing in China— the impact of socioeconomic development; water pricing in Colombia—the transition from bankruptcy to full-cost recovery; water pricing in France—moving toward more incentives to conserve water; water pricing experiences in India—emerging issues; water pricing in Israel—various waters, various neighbors; water pricing in Italy—beyond full-cost recovery; water pricing in Mexico—pricing structures and implications; water pricing in the Netherlands; New Zealand water pricing; water pricing—the case of South Africa; water pricing in Spain—following the footsteps of somber climate change projections; introducing new mechanisms into water pricing reforms in China; how to integrate social objectives into water pricing; sustainable water rate design at the Western Municipal Water District—the art of revenue recovery, water use efficiency, and customer equity; pricing urban water services in the developing world—the case of Guayaquil, Ecuador; the price for domestic water supply—an innovative method developed for the Tucano aquifer in the state of Bahia, Brazil; pricing for reclaimed water in Valencia, Spain—externalities and cost recovery; and pricing municipal water and wastewater services in developing countries—whether utilities are making progress toward sustainability. Dinar is a professor of environmental economics and policy with the School of Public Policy at the University of California, Riverside. Pochat is a professor at the National University of Litoral. Albiac-Murillo is a researcher at the Agrifood Research and Technology Center and a professor at the University of Zaragoza.”
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.417 | 0.403 |
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".