Can Informal Water Vendors be Trusted? The Evolution of Informality in Kisumu’s Delegated Management Model
Bibliographic record
Abstract
In the world’s lower income regions, piped water systems have failed to keep pace with growing populations. A wide variety of informal water vendors are playing an important role in filling these gaps, which are projected to grow as the urban population increases to 2.5 billion by 2050. Though informal water vendors provide essential services that can be agile and locally managed, vending comes with potential risks, including those related to affordability and poor water quality. This has lead scholars and practitioners alike to ask: under what conditions can water vendors be trusted? We unpack this question in Kisumu, Kenya, where consumers are served by a diversity of actors that may work with or independently of the utility. We ask how coordination between the utility and informal water vendors affects the performance of co-produced water services, investigating performance in terms of trust and trustworthiness. Our results show that partnerships with informal providers offer opportunities to leverage social trust and relationships, bolstering the capacity of institutions to deliver on their objectives of safe, affordable water. These insights are important in ensuring the human right to water as rapidly growing cities rely on a patchwork of piped and off-grid water supplies.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".