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Record W2768876705 · doi:10.1080/1554477x.2017.1375786

Child Marriage Laws around the World: Minimum Marriage Age, Legal Exceptions, and Gender Disparities

2017· article· en· W2768876705 on OpenAlexaff
Megan Arthur, Alison Earle, Amy Raub, Ilona Vincent, Efe Atabay, Isabel Latz, Gabriella Kranz, Arijit Nandi, Jody Heymann

Bibliographic record

VenueJournal of Women Politics & Policy · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicDemographic Trends and Gender Preferences
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersBill and Melinda Gates Foundation
KeywordsLegislationPolitical scienceLawDemographic economicsEconomics

Abstract

fetched live from OpenAlex

The marriage of children below 18 is widely recognized in international human rights agreements as a discriminatory global practice that hinders the development and well-being of hundreds of millions of girls. Using a new global policy database, we analyze national legislation regarding minimum marriage age, exceptions permitting marriage at earlier ages, and gender disparities in laws. While our longitudinal data indicate improvements in frequencies of countries with legal provisions that prohibit marriage below the age of 18, important gaps remain in eliminating legal exceptions and gender discrimination.

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)0.001
metaresearch head score (Gemma)0.007
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 score0.046
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.042
GPT teacher head0.353
Teacher spread0.311 · 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

Citations132
Published2017
Admission routes1
Has abstractyes

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