Strengthening Child Marriage Prevention Policy Strategies in the Implementation of Development Planning
Bibliographic record
Abstract
Child marriage is a form of child abuse with multifaceted impacts. The child marriage rate was included as a development target in the 2020-2024 National Medium-Term Development Plan (RPJMN) and was achieved in 2024. This target is supported by various cross-sectoral efforts at both the national and regional levels. However, challenges remain, such as unregistered child marriages, teenage births, and a suboptimal referral system for child victims and integrated data. In the next planning document, the 2025-2029 RPJMN, the child marriage rate remains a priority indicator, necessitating a re-sharpening of the strategy to ensure that efforts are not only quantitative but also qualitative, ensuring that every child's rights are met. This policy brief is designed to provide recommendations for alternative policies to strengthen child marriage prevention efforts through a qualitative approach with descriptive analysis to illustrate achievements and identify challenges, systematic analysis to generate policy alternatives, and assessment of priority policy alternatives using five decision-making and public policy criteria. The evaluation results indicate strengthening norms in various regulations, a clear role structure between stakeholders, and collaborative processes at both the national and regional levels. To address future challenges, a transformation is needed by expanding the scope of policy from prevention to prevention and management of child marriage. Four alternative strategies were identified: strengthening asymmetric policies tailored to regional characteristics, enhancing reproductive health education to increase adolescent resilience and raise awareness in the immediate environment, strengthening referral services for victims of child marriage, and improving the integrated data system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".