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Record W4364354467 · doi:10.1080/00324728.2023.2181383

Increases in child marriage among the poorest in Mali: ‘Reverse policies’ or data quality issues?

2023· article· en· W4364354467 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenuePopulation Studies · 2023
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research CouncilWeatherhead Center for International Affairs, Harvard UniversitySocial Sciences and Humanities Research Council of CanadaHorizon 2020 Framework ProgrammeMax-Planck-GesellschaftJacobs FoundationUniversity of PennsylvaniaYork UniversityMcGill UniversityNew York University Abu DhabiNational Science Foundation
KeywordsChild marriageData collectionDemographic economicsDeveloping countryEconomic growthDemographyPsychologySocioeconomicsDevelopment economicsPolitical scienceEconomicsSociologySocial science

Abstract

fetched live from OpenAlex

Child marriage is associated with adverse outcomes related to women’s well-being. Many countries have introduced laws banning this practice, and a number of studies have evaluated their impact. Scant research has focused on instances where countries have lowered the legal minimum age at marriage, even though such ‘reverse policies’ could result in stalled or uneven progress in eradicating child marriage. Using visualization techniques, regression analyses, and multiple robustness checks, we document changes in the prevalence of child marriage in Mali, where in 2011 the general minimum age at marriage of 18 was lowered to 16. Since 2011, the prevalence of child marriage has progressively increased among women with no education and women living in communities characterized by low local development. We reflect on the role that data collection processes may play in explaining some of these findings and stress how repealing existing provisions aiming to protect girls can have adverse consequences on the most vulnerable social strata.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.038
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

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

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 head0.206
GPT teacher head0.481
Teacher spread0.275 · 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