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Record W2948555140 · doi:10.14507/epaa.27.3385

Educação, pobreza e programas de transferência de renda: A implementação do Programa Oportunidades no México

2019· article· pt· W2948555140 on OpenAlexfundno aff
Breynner Ricardo de Oliveira, Maria do Carmo de Lacerda Peixoto

Bibliographic record

VenueEducation Policy Analysis Archives · 2019
Typearticle
Languagept
FieldSocial Sciences
TopicPoverty, Education, and Child Welfare
Canadian institutionsnot available
FundersPetroleum Technology Research Centre
KeywordsPolitical scienceSociologyHumanitiesArt

Abstract

fetched live from OpenAlex

Este artigo analisa a implementação do Programa Oportunidades no México a partir da perpectiva dos agentes de base vinculados a condicionalidade educacional. Ao adotar a abordagem bottom up para analisar a implementação, a formulação de Lipsky (1980) é o referencial que estrutura o estudo. Os profissionais da educação, os promotores comunitários e as representantes das titulares são os que correspondem à caracterização do autor nesse caso. Foram realizadas 47 entrevistas: nove com o corpo estratégico e gerencial nos níveis nacional e estadual; 38 com agentes locais (duas com profissionais da saúde; 14 com atores da escola, 10 com agentes vinculados ao acompanhamento das famílias e 12 com vocales. Os roteiros para as entrevistas foram adaptados conforme sua posição na hierarquia do programa, contemplando as categorias: desenho institucional e sua operação; percepções sobre o programa e o público-alvo; impressões sobre a condicionalidade educacional e as exigências impostas aos agentes locais. Os dados revelam que a condicionalidade educacional mobilizou os agentes locais, apesar das dificuldades de cooperação institucional, das disputas políticas, das limitações orçamentárias, das oscilações na hierarquização das prioridades sociais e da tutela e dependência, evidenciando aspectos que estão diretamente ligados ao processo de formação do Estado e da sociedade mexicanos.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.351
Teacher spread0.332 · 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.

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
Published2019
Admission routes1
Has abstractyes

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