INOVASI “LAPORRAMA” DALAM PELAYANAN PUBLIK DINAS KEPENDUDUKAN DAN PENCATATAN SIPIL KABUPATEN SINTANG
Bibliographic record
Abstract
This research examines the implementation of LAPORRAMA, an innovative digital system introduced by the Disdukcapil of Sintang Regency, aimed at enhancing public satisfaction in civil registration services, particularly for birth and death certificates. The study investigates key issues such as accessibility, data accuracy, and transparency in service delivery within rural areas, where geographical challenges hinder access to essential documentation. Employing qualitative methods, including interviews with key stakeholders such as health officials and community leaders, the findings reveal that LAPORRAMA significantly streamlines administrative processes, reducing the time and effort required for obtaining vital records. Notably, the integration of this system facilitates immediate issuance of birth certificates at local health facilities, improving user experiences and mitigating the bureaucratic burdens associated with traditional manual processes. Additionally, LAPORRAMA addresses issues of corruption by ensuring transparency in administrative fees. Overall, the research highlights LAPORRAMA's critical role in enhancing the quality of public services and fostering trust in government institutions, thereby contributing positively to community well-being in Sintang Regency.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".