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Record W4200497260 · doi:10.6000/1929-4409.2021.10.177

Gender Equality: Challenges toward Efforts to Minimize Child Marriage in Indonesia during the Covid-19 Pandemic

2021· article· en· W4200497260 on OpenAlexvenueno aff
Sonny Dewi Judiasih, Nyulistiowati Suryanti, Sudaryat Sudaryat, Deviana Yuanitasari

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGender and Women's Rights
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)PandemicCoronavirus disease 2019 (COVID-19)Marriage lawInequalityChild marriageLawSociologyPolitical scienceEconomic growthDemographyEconomicsMedicinePopulationMathematics

Abstract

fetched live from OpenAlex

The practice of child marriage in Indonesia is a serious problem that must be resolved. This is a problem faced in various countries in the world. The SDG's programs include achieving gender equality and empowering women and girls, with a target to achieve the abolition of child marriage by 2030. The research method used is a social-legal approach. The purpose of this study is to find out the government's efforts in overcoming the problem of gender inequality in the age requirements for marriage in Indonesia and the application for dispensation for marriage during the Covid-19 pandemic in Indonesia. The old Marriage Law stipulates that the age of marriage for men is 19 years and for women 16 years. The government then changed this provision through Law Number 16 of 2019 concerning Marriage, in which the marriage age for men and women is the same, which is 19 years. During the Covid-19 pandemic, applications for marriage dispensation in Indonesia indicated a sharp increase. This means that the application for marriage dispensation is unaffected by the Covid-19 pandemic situation. The existence of exceptions through dispensation efforts makes the requirements for the age of marriage can still be deviated, so that gender equality, which is expected to minimize child marriages above, cannot be realized or cannot be carried out as desired.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.465
Threshold uncertainty score0.304

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.187
GPT teacher head0.391
Teacher spread0.204 · 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.

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
Published2021
Admission routes1
Has abstractyes

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