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Avaliação de Impacto das condicionalidades de educação do Programa Bolsa Família (2005 e 2009)

2013· article· pt· W2057099168 on OpenAlexfundno aff
Ernesto F. L. Amaral, Vinícius do Prado Monteiro

Bibliographic record

VenueDados · 2013
Typearticle
Languagept
FieldSocial Sciences
TopicPoverty, Education, and Child Welfare
Canadian institutionsnot available
FundersMinisterio de Economía y CompetitividadMinistério da EducaçãoCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorYork University
KeywordsSchool dropoutChristian ministryDropout (neural networks)GeographyWelfare economicsDemographic economicsPolitical scienceSocioeconomicsSociologyEconomics

Abstract

fetched live from OpenAlex

Dans cet article, on examine les impacts des conditionnalités de l'éducation dans le Programme Bolsa Família sur l'absentéisme scolaire d'enfants qui bénéficient de ce programme. L'hypothèse principale est que l'enfant qui habite dans un foyer recevant cette aide a moins de chances d'abandonner l'école. On se sert de données de l'Étude de l'impact du Programme Bolsa Família (AIBF) de 2005 à 2009 du Ministère du Développement Social et de la Lutte contre la Faim (MDS). Des modèles logistiques ont estimé les chances d'abandon scolaire de 2005 à 2009, à partir de trois niveaux de revenu domiciliaire par habitant, compte tenu des caractéristiques du foyer, de la mère et de l'enfant. Les enfants habitant dans des foyers bénéficiaires du Programme Bolsa Família ont révélé une nette réduction du taux d'abandon scolaire en 2005. Les données pour 2009 n'ont pas été statistiquement significatives, bien que montrant une diminution de l'abandon scolaire comme résultat de l'aide reçue du Bolsa Família.

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)0.003
metaresearch head score (Gemma)0.009
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 score0.137
Threshold uncertainty score0.273

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.022
GPT teacher head0.335
Teacher spread0.313 · 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

Citations14
Published2013
Admission routes1
Has abstractyes

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