Reframing the Challenges and Opportunities for Improved Sanitation Services in Eastern Africa Through Sustainability Science
Bibliographic record
Abstract
Sustainable sanitation services are still unavailable to most people in Sub-Saharan Africa (SSA) despite decades of implementing very diverse sanitation projects across the continent. Using a Sustainability Science lens, this chapter identifies through an extended literature review the drivers and shortcomings of business-as-usual sanitation approaches that tend to fail in SSA. As one of the main challenges for the success of sanitation project is the creation of an enabling environment, we attempt to identify some of the critical elements that could support the development of such an environment. Subsequently we identify characteristics and competencies conducive to breaking the cycle of failure and to developing sustainable sanitation systems. We use data from key informant interviews with sanitation implementers, focus group discussions with sanitation facility users and visits to sanitation project sites in Kenya, Tanzania and Uganda. The sanitation approaches explored, although different, are all characterized by their adaptation to the local context, community participation, built-in mechanisms that ensure financial viability, use of technologies that are culturally appropriate and emphasis on environmental sustainability. We offer several policy and practice recommendations for the development of successful sanitation governance structures for national governments, external support agencies and project implementers. The examples discussed in this chapter show promise, but do not guarantee success, as all solutions will require several iterations to adaptate to the local context, as well as financial and governance support, to be scaled up.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".