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Record W2558512557 · doi:10.1111/1559-8918.2016.01088

What Is a Sustainable Innovation? Cultural and Contextual Discoveries in the Social Ecology of Cooking in an African Slum

2016· article· en· W2558512557 on OpenAlexaff
WILLIAM SCHINDHELM GEORG, Peter Jones

Bibliographic record

VenueEthnographic Praxis in Industry Conference Proceedings · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsOntario College of Art and DesignArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsSubsistence agricultureSlumIndigenousContext (archaeology)EthnographyPovertySociologySustainable livingEveryday lifeSustainabilityEcologyEconomic growthPolitical scienceGeographyEconomicsAnthropology

Abstract

fetched live from OpenAlex

This paper investigates how a close understanding of human activity can inform the design of culturally and contextually sustainable innovations for subsistence markets. Building on existing literature related to poverty alleviation initiatives and an ethnographic field study, this project attempted to understand the cultural and contextual challenges to the substitution of unhealthy and unsustainable biomass as cooking fuels by cleaner and competitive cooking alternatives in Kitintale, an urban slum in Kampala, Uganda. We share new research findings and experience from a recent ethnographic study that reveals the incompatibility of modern innovation theory with the realities of the deeply knitted everyday practices in the social ecology of slum life. As the findings of this project suggest, broad claims that disruptive innovation can shift existing practices, change demand and displace market leaders through the creation of new value networks might not fully apply in a context where the existence of cultural patterns have shaped the evolution of indigenous solutions over generations, and reactivity to daily circumstances is high.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.466
Threshold uncertainty score0.469

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.004
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 head0.050
GPT teacher head0.305
Teacher spread0.254 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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