Bibliographic record
Abstract
The use of benchmarking and performance indicators to evaluate and compare water operators is a relatively new phenomenon. It has been taking place in the private sector since the 1970s but only migrated to public services over the past two decades. It is now widespread in the water sector, but there are emerging concerns about its commercial bias and relatively undemocratic processes. This paper reviews the history of benchmarking in the water sector, discusses arguments for and against its use, and proposes an alternative performance evaluation framework that may help to better account for universality, sustainability and democratic forms of governance, particularly with public water operators in low-income settings in the global South. I argue that shared forms of performance measurement can be useful but only if they are more explicit about recognizing local difference and if they become better at promoting public awareness and advancing equity. The paper also asks why existing water benchmarking systems do not explicitly differentiate between public and private water operators, and proposes indicators that may help promote non-commercialized forms of public water services. The proposals are necessarily tentative and preliminary – calling for more empirical and theoretical research on the topic – but do offer concrete alternative benchmarking possibilities for further debate and exploration.
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 | 0.032 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".