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Record W2535040021 · doi:10.1596/978-1-4648-0902-6_ch3

Female Labor Force Participation and Labor Market Outcomes in Latin America and the Caribbean

2016· book-chapter· en· W2535040021 on OpenAlexaboutno aff
Mercedes Mateo Díaz, Lourdes Rodríguez-Chamussy

Bibliographic record

VenueThe World Bank eBooks · 2016
Typebook-chapter
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsLatin AmericansEarningsUnemploymentPolitical scienceSocial securityInformal sectorProductivityEconomicsLabour economicsDemographic economicsEconomic growth

Abstract

fetched live from OpenAlex

Describes women’s participation and outcomes in the labor market in Latin America and the Caribbean (LAC) relative to men’s across the life cycle, comparing results across countries, looking at the dynamics of mothers’ and fathers’ behavior in the labor market, and identifying patterns of women’s engagement in paid employment, such as segregation by employment status and sector. Despite the closing of the education gap between boys and girls in the region, women’s participation in the labor force remains much lower than men’s, and even countries that have made significant progress in economic participation for women have not achieved parity. In Brazil and Costa Rica, where gender gaps in access to education parallel those in the Netherlands and Canada, women’s economic participation remains significantly lower than men’s. The gender gap in earnings persists, as does women’s higher vulnerability to unemployment; women hold more informal and precarious jobs; and the jobs women hold are concentrated in lower productivity sectors.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.075
Threshold uncertainty score0.150

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.001

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 head0.272
Teacher spread0.251 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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