Perceptions of Inclusivity and Sustainability in Urban Sanitation in Global South Cities
Bibliographic record
Abstract
Citywide Inclusive Sanitation (CWIS) calls for sustainable urban sanitation services for all, but the definitions of "inclusion" and "sustainability" within the framework leave room for interpretation. This study aims to provide an initial understanding of how these terms are currently interpreted by a range of sanitation actors in six cities of the Global South. Urban sanitation professionals from private (n = 16), public (n = 28), non-governmental (n = 43), and academic (n = 10) institutions were interviewed using a standardized tool, and data was analyzed to identify themes and trends. Terms such as "everyone" or "for all" shed little light on how to ensure inclusion, though disabled people, women, children, and the poor were all highlighted when probed. Greater specificity of beneficiary groups in policy is likely to enhance their visibility within sanitation service provision. All three pillars of sustainability identified within CWIS were referenced, with different stakeholders focusing more closely on environmental, social, or economic sustainability, based on their organizational goals and interests. Greater collaboration may foster a balanced view across the pillars, with different organizations acting as champions for each one. The findings can facilitate discussions on a shared understanding of multi-stakeholder engagement in achieving inclusive and sustainable sanitation service provision.
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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 | 0.004 | 0.005 |
| 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 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".