More sustainable systems through consolidation? The changing landscape of rural drinking water service delivery in Uganda
Bibliographic record
Abstract
The drinking water services sector in Uganda is in the early stages of a nationally planned transition; it aims to move from a paradigm based on community managed point sources towards one of professional utilities of piped networks. The implementation of this transition was studied in Western Uganda’s Kabarole District between 2017 and 2019; a systems approach (building blocks) was used to assess the sustainability of the different service models. The level of services was assessed using household and infrastructure surveys; these were supplemented by a management assessment, key informant interviews and stakeholder workshops. The two utility models present in Kabarole outperformed the community management model, with the existing national utility demonstrating greater maturity and performance than the newer Umbrella utility. The community management model, while relatively well defined in policy and planning frameworks, was poorly implemented, with less than 20% of community management structures operational at water points. The water sector is undergoing a process of consolidation of service delivery under a smaller number of larger providers, a trend that has been observed in other countries as they progress towards universal supply. In this paper, the prospects and risks of the current sector trajectory are discussed, as are the implications for monitoring, regulation and planning systems across the urban–rural spectrum.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".