A review of frontier approaches to efficiency and productivity measurement in urban water utilities
Why this work is in the frame
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Bibliographic record
Abstract
Despite the rapid global revitalisation of urban water policy, and the universal need to measure and improve organizational efficiency and productivity in all suppliers as a means of ensuring the sustainability of this key resource, only recently have the most advanced econometric and mathematical programming frontier techniques been applied to urban water utilities. This paper provides a synoptic survey of the comparatively few empirical analyses of frontier efficiency and productivity measurement in urban water utilities in Australia, the UK, Spain, the US, Mexico, Brazil, Canada, Germany, Italy, Malaysia and Slovenia, among others. The survey examines both estimation and measurement techniques and the non-discretionary structural and regulatory determinants of efficiency and productivity. There is particular focus on how the results of past studies inform regulatory policy and managerial behaviour and key directions for future research.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it