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Record W2757463499 · doi:10.2495/sdp-v13-n3-373-381

Empowerment versus charity in bringing microflush toilets to the poor: Sustainable local economies for the common good

2018· article· en· W2757463499 on OpenAlexvenueno aff
S. Mecca, Alejandro Pedro Ayala

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsEmpowermentBusinessSustainable developmentNatural resource economicsEnvironmental planningEconomic growthEconomicsPolitical scienceEnvironmental scienceLaw

Abstract

fetched live from OpenAlex

The GSAP (Grand Challenges Explorations Award) Microflush toilet system, a locally sourced-locally fabricated toilet that features a macro-organism enhanced aerobic filter-digester and an innovative valve that flushes on just 150 cc of water has proven to be an effective sustainable sanitation solution for developing world tropical communities. This sustainable, locally sourced, locally fabricated technology has been deployed in 18 countries around the world through trained MAKERs creating for them a local business opportunity. The household toilet averages $300 of which $100 is profit for the MAKER. For the lowest income quintile households, a microloan is often required to effect toilet ownership and, when such credit is unavailable or inaccessible, GSAP's LENDER model, operated by the MAKER or by another community player becomes a viable solution. Recent data from this intervention suggests a more powerful investment model to empower multiple local MAKERs with a sustainable sanitation credit fund (SANCRED). GSAP's SANCRED approach as it evolved from its LENDER model is described. Early experience with essential elements of the model deployed with teams of Maasai women MAKERs is presented. The potential of the models, which emphasize economic development for the common good, solving the menacing condition of sanitation in the world and meeting Goal #6 and several others of the UN Sustainable Development Goals (SDGs) are noted.

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)0.002
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.571
Threshold uncertainty score0.654

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.029
GPT teacher head0.310
Teacher spread0.281 · 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 designQualitative
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

Citations3
Published2018
Admission routes1
Has abstractyes

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