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Record W4214736120 · doi:10.1080/19439342.2022.2042358

‘Craft for social good’: do on the job training of artisans’ impact on their vulnerability to poverty? Evidence from Kibera

2022· article· en· W4214736120 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Development Effectiveness · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsWorld University Service of Canada
Fundersnot available
KeywordsPovertyVulnerability (computing)Propensity score matchingCraftMatching (statistics)SocioeconomicsDemographic economicsGeographyEconomicsEconomic growthMedicineComputer science

Abstract

fetched live from OpenAlex

In this paper, We study how an on the job training program targeting low-income jewellery artisans (SOKO) impact on their vulnerability to poverty in Kenya. We use propensity score matching to assess SOKO’s impact on the poverty likelihood of artisans. We find that SOKO artisans have a lower vulnerability to poverty as compared to artisans with similar socio-demographic characteristics but no SOKO affiliation. We also find that female artisans affiliated with SOKO are more vulnerable to poverty as compared to male artisans. Our findings are robust to various propensity score matching specifications and to alternative measures of vulnerability to poverty.

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.

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)0.005
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 score0.641
Threshold uncertainty score0.598

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
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 science0.0000.000
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 head0.060
GPT teacher head0.300
Teacher spread0.239 · 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