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Record W2141978053 · doi:10.1142/s1084946713500040

EVALUATING THE GENDER VARIATIONS IN INFORMAL SECTOR ENTREPRENEURSHIP: SOME LESSONS FROM BRAZIL

2013· article· en· W2141978053 on OpenAlexaff
Colin C. Williams, Youssef Youssef

Bibliographic record

VenueJournal of Developmental Entrepreneurship · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicTaxation and Compliance Studies
Canadian institutionsHumber Polytechnic
Fundersnot available
KeywordsInformal sectorEntrepreneurshipRealmFemale entrepreneursEconomic growthBusinessDeveloping countryWomen entrepreneursLabour economicsEconomicsPolitical scienceFinance

Abstract

fetched live from OpenAlex

The aim of this paper is to evaluate critically the gender variations in informal sector entrepreneurship. Until now, a widely-held belief has been that entrepreneurs operating in the informal sector in developing nations are lowly paid, poorly educated, marginalized populations doing so out of necessity as a survival strategy in the absence of alternatives. Reporting an extensive 2003 survey conducted in urban Brazil of informal sector entrepreneurs operating micro-enterprises with five or less employees, the finding is that although less than half of these entrepreneurs are driven out of necessity into entrepreneurial endeavor in the informal economy, women are more commonly necessity-driven entrepreneurs and receive lower incomes from their entrepreneurial endeavor than men despite being better educated. The outcome is a call to recognize how the gender disparities in the wider labor market are mirrored and reinforced by the participation of men and women in the realm of informal sector entrepreneurship.

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)0.005
metaresearch head score (Gemma)0.014
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.182

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.123
GPT teacher head0.303
Teacher spread0.180 · 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

Citations84
Published2013
Admission routes1
Has abstractyes

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