PROCESS DIMENSION ANALYSIS ON THE SUCCESS OF CHILD MARRIAGE PREVENTION POLICY
Bibliographic record
Abstract
Children are the foundation of a nation's human resources, an important issue affecting child welfare is child marriage. The East Java Provincial Government issued a policy in the form of a Governor's Circular Letter (SE) on the prevention of child marriage, Malang Regency as an area with a high marriage rate in East Java followed up on the SE. After three years, the efforts made have succeeded in reducing the number of child marriages in Malang District each year. This article analyzes the follow-up process of the child marriage prevention policy in Malang District using a descriptive qualitative research method, case study research type with Allan McConnell's Policy Success theory as the analytical knife. This article explores the success of the child marriage prevention policy from the process dimension, as well as the level of success using the degree of policy success/failure. The results showed the success of the policy in the process dimension, firstly maintaining policy objectives / instruments, all SE Child Marriage Prevention objectives were maintained with several derivative policies, although there was one policy instrument related to the budget that was not fully maintained. The level of success is included in Resilient Success. Second, ensuring policy legitimacy, where the entire SE follow-up process is legitimized by all parties, so the success level is included in Success. Third, building a sustainable coalition, the Malang District Government built a sustainable coalition to overcome the problem of child marriage through the policies made. The success rate is also included in Success.
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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 | 0.018 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".