Measuring the Technical Efficiency of Municipal Water Suppliers: The Role of Environmental Factors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper extends the multistage procedure set out by Fried, Schmidt, and Yaisawarng (1999) to examine the importance of environmental factors when assessing the technical efficiency of water agencies. However, following Simar and Wilson’s (2007) critique of multistage efficiency analyses, the paper uses a bootstrapping approach in order to have consistent inference. Data are from a cross-section of municipal water agencies in Ontario, Canada, during 1996. The main findings are that environmental factors explain some of the observed variation in efficiency scores and that water agencies’ relative efficiency scores are changed substantially after controlling for environmental factors. <i>(JEL H42, Q25)</i>
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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