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Implementation Of Birth Certificate Issuance Service Improvement Program (A Study of Permendagri No. 9 of 2016 in the Population and Civil Registration Office)

2020· article· en· W3047840220 on OpenAlexaff
Rizki Fillya Curtinawati, Agus Suryono, Andy Fefta Wijaya

Bibliographic record

VenueJurnal Ilmiah Administrasi Publik · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicLocal Governance and Development
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsMandateCertificateBusinessBirth certificatePopulationGovernment (linguistics)Service (business)Service delivery frameworkMarketingComputer scienceEnvironmental healthMedicinePolitical science

Abstract

fetched live from OpenAlex

This study aims to analyze the implementation of a program to improve birth certificate issuance services at the Population and Civil Registration Office in Madiun Regency as a mandate from Permendagri No. 9 of 2016 concerning the Acceleration of Increasing the Coverage of Birth Certificate Ownership and to know the supporting and inhibiting factors. The author uses the indicator component of PermenPAN-RB No. 38 of 2012 concerning guidelines for evaluating public service performance. The results showed that one out of three excellent programs have not yet been felt beneficial. Based on analysis of PermenPAN-RB Regulation No.38 of 2012, there are six of nine indicators in the study results that were not run optimally. Supporting factors include budget, government support, and collaboration with village officials. The inhibiting factors are geographical location, human resources, communication/coordination, community awareness, semi-online services, data management, and population event reports at the village level

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.067
GPT teacher head0.359
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2020
Admission routes1
Has abstractyes

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