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Record W3137712745 · doi:10.1080/14616742.2021.1895862

Gender, the World Bank, and conditional cash transfers in Latin America

2021· article· en· W3137712745 on OpenAlexafffund
Nora Nagels

Bibliographic record

VenueInternational Feminist Journal of Politics · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicPoverty, Education, and Child Welfare
Canadian institutionsUniversité du Québec à Montréal
FundersFonds de Recherche du Québec-Société et Culture
KeywordsConditional cash transferLatin AmericansEmpowermentPovertyArgument (complex analysis)SubsidyPolitical scienceGender equalityNorm (philosophy)Cash transfersInequalityEconomic growthSociologyEconomicsGender studiesLaw

Abstract

fetched live from OpenAlex

Since the mid-1990s, nearly all of the countries in Latin America have adopted a conditional cash transfer (CCT) program. These critical programs – which have become the standard poverty reduction policy across the region – provide subsidies to poor mothers on the condition that they enroll their children in school and take them for health check-ups. The first and norm leader program, Mexico’s Progresa, included gender equality and empowerment of women as part of its original design goals. However, since Progresa, no Latin American CCT program has been designed with reduction of gender inequalities in mind. Feminist scholars have critiqued these programs as “maternalist.” What happened to the goals of gender equality along the way? This article sheds light on the World Bank’s involvement in weakening the gender equality goals that were an integral part of the original policy design. In redesigning CCT programs, the World Bank has sidelined these goals in two significant ways. First, policy entrepreneurs, committed to evidence-based policies, have omitted the female empowerment argument in response to mixed results on the matter of gender and CCTs. Second, gender experts (and gender knowledge) at the World Bank have been marginalized in favor of their economist and development expert colleagues.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.841
Threshold uncertainty score0.591

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.328
Teacher spread0.300 · 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 designTheoretical or conceptual
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

Citations21
Published2021
Admission routes2
Has abstractyes

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