Gender Equality: Challenges toward Efforts to Minimize Child Marriage in Indonesia during the Covid-19 Pandemic
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".