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Record W2034927594 · doi:10.1080/19376812.2013.838687

Informal economic activity in Kenya: benefits and drawbacks

2013· article· en· W2034927594 on OpenAlexaff
Kempe Ronald Hope

Bibliographic record

VenueAfrican Geographical Review · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicTaxation and Compliance Studies
Canadian institutionsInternational Development Research Centre
Fundersnot available
KeywordsInformal sectorRealmWork (physics)BusinessService (business)Economic growthScale (ratio)EconomicsMarketingPolitical scienceEngineering

Abstract

fetched live from OpenAlex

The informal economy now constitutes an important component in the economic activities and process of development in Kenya. Although its relative importance was minimized in the past, the informal economy continues to thrive in Kenya and the rest of Africa. In this work, the informal economy (Jua Kali sector) is defined as consisting of those economic activities, units, enterprises and workers (both professionals and non-professionals) who engage in commercial activities outside of the realm of the formally established mechanisms for the conduct of such activities and are therefore not regulated or protected by the State. It includes all forms of unregistered or unincorporated small-scale productive, vending, financial and service activities, and is also comprised of all forms of employment without secure contracts, worker benefits or social protection both inside and outside informal enterprises. The article discusses and analyses the nature, impact, benefits and drawbacks of informal economic activity in Kenya.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.021
GPT teacher head0.235
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), 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

Citations23
Published2013
Admission routes1
Has abstractyes

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