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Record W4310777422 · doi:10.1177/11786302221139964

Perceptions of Inclusivity and Sustainability in Urban Sanitation in Global South Cities

2022· article· en· W4310777422 on OpenAlexaff
Kondwani Luwe, Rebecca Sindall, Flor Y. Garcia-Becerra, Annatoria Chinyama, Huda Lohiya, Caitlin Hope, Fryderyk Paczkowski, Hans C. Komakech, Tracy Morse

Bibliographic record

VenueEnvironmental Health Insights · 2022
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsUniversity of Northern British Columbia
FundersRoyal Academy of EngineeringGovernment of the United KingdomConsejo Nacional de Ciencia y TecnologíaScottish Funding CouncilUniversity of Strathclyde
KeywordsSanitationSustainabilityInclusion (mineral)BeneficiaryStakeholderPublic relationsBusinessEnvironmental planningEconomic growthPolitical scienceEnvironmental resource managementMedicineSociologyGeographyEconomicsSocial science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.474

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.274
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2022
Admission routes1
Has abstractyes

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