Pengaruh Kepemilikan Akta Kelahiran Dan Akta Perkawinan Terhadap Capaian Kepemilikan Dokumen Administrasi Kependudukan Kabupaten Sikka Tahun 2023
Bibliographic record
Abstract
Population administration in Indonesia involves managing various aspects such as birth, marriage, death and identity. These records ensure legal identity and facilitate access to public services. In Sikka district, there is a significant shortage of birth certificates and marriage certificates with 52.83% of the population not having birth certificates by 2023 and 46.40% of married couples not having marriage certificates. This Policy Paper aims to achieve birth certificate and marriage certificate registration coverage in Sikka District by 2024 and develop policy recommendations to improve coverage. A quantitative approach is used to analyze the percentage of birth certificate and marriage certificate processing in 2023. In-depth interviews with civil registration officers and residents to identify challenges and perceptions related to the registration process were also conducted. The results of the analysis show that there is a significant gap in the ownership of birth certificates and marriage certificates in various sub-districts in Sikka district with contributing factors including many people not realizing the importance and benefits of having these documents, remote areas facing difficulties accessing civil registration services, the cost of going to the civil registry office is high and traditional/customary practices over legal documentation. To address this, the policy recommendations are the Sikka Regent Decree on the ASANTT application follow-up work plan and establishing mobile registration units and increasing the number of registration offices in remote areas.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.008 |
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".