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Record W2888978570 · doi:10.5539/ies.v11n9p36

Evaluating the Role of Education as a Birth Control Policy in Burkina Faso: A Propensity Score Weighting Approach

2018· article· en· W2888978570 on OpenAlexaffvenue
David Zoundi, Jean-Louis Bago, Wamadini dite Minata Souratié, Miaba Louise Lompo

Bibliographic record

VenueInternational Education Studies · 2018
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsPropensity score matchingCounterfactual thinkingFertilityEstimationWeightingDemographyControl (management)Identification (biology)PsychologyStatisticsPopulationEconomicsMedicineSociologySocial psychologyMathematics

Abstract

fetched live from OpenAlex

We use the 2014 round of Burkina Faso’s Demographic and Health Surveys (DHS) to identify and quantify the causal effect of women’s education on their fertility outcomes focusing on two fertility indicators: the total number of children ever born and the age at first birth. However, women's educational attainments may reflect the difference in term of access to schooling or individual characteristics such the family wealth, causing a threat to the empirical identification. In order to achieve consistent estimation, our empirical strategy follows Imbens (2000) and uses the propensity score weighting (PSW) approach to generate an appropriate counterfactual group accounting for education levels. Results from the PSW estimation suggest that education reduces the number of children per woman and delays women’s first birth in Burkina Faso. Hence, promoting girls education is an efficient policy to achieve birth control in Burkina Faso.

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)0.043
metaresearch head score (Gemma)0.066
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 score0.043
Threshold uncertainty score0.225

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0430.066
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.085
GPT teacher head0.440
Teacher spread0.355 · 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
Published2018
Admission routes2
Has abstractyes

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Same venueInternational Education Studies→Same topicGlobal Maternal and Child Health→French-language works237,207→