MétaCan
Menu
Back to cohort
Record W4254222315 · doi:10.31219/osf.io/cf5nu

Implementasi Kebijakan Pembentukan Kabupaten/Kota Layak Anak Pada Bidang Pendidikan dan Kesehatan di Kabupaten Pandeglang

2018· preprint· id· W4254222315 on OpenAlexaff
Raden Dewi Setiani

Bibliographic record

Venuenot available
Typepreprint
Languageid
FieldMedicine
TopicPublic Health and Nutrition
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Latar belakang penelitian adalah terdapat hambatan pemenuhan hak anak untuk mewujudkan KLA di Kabupaten Pandeglang Bidang pendidikan dan kesehatan, Tujuan penelitian untuk menganalisa implementasi kebijakan pembentukan KLA, menganalisa faktor yang mendukung dan menghambat pelaksanaan kebijakan pembentukan KLA Bidang Pendidikan dan Kesehatan di Kabupaten Pandeglang.Metode penelitian menggunakan pendekatan kualitatif dengan teknik pengumpulan data melalui Wawancara, Observasi dan Studi Dokumen. Sebagai narasumber ada 8 informan Stakeholder KLA Pandeglang. Teknik analisis data dilakukan dengan memahami dan menyusun data yang telah diperoleh secara sistematis menggunakan Open Coding (Pengodean Terbuka), Axial Coding (Pengodean Berporos), Selective Coding (Pengodean Selektif).Berdasarkan dimensi implementasi yang dikemukakan oleh Van Metter & Van Horn yang mencakup aspek ukuran dan tujuan kebijakan, sumber daya, karakteristik agen pelaksana, sikap dan kecenderungan para pelaksana, komunikasi antar organisasi dan aktivitas pelaksana, serta lingkungan ekonomi, sosial dan politik dalam implementasi Kebijakan Pembentukan KLA pada Bidang Pendidikan dan Kesehatan di Kabupaten Pandeglang. Hasil penelitian secara umum sudah cukup baik dilihat dari keseriusan Pemkab Pandeglang dengan mengeluarkan kebijakan melalui Keputusan Bupati tentang Gugus Tugas sebagai upaya untuk membentuk KLA di Kabupaten Pandeglang dalam rangka pemenuhan hak-hak anak, walaupun pemenuhan hak anak masih belum maksimal dengan tidak adanya alokasi anggaran khusus, terbatasnya ruang tempat anak serta kurangnya pengetahuan dan pemahaman masyarakat tentang KLA.Kata Kunci : Implementasi Kebijakan, Kabupaten/Kota Layak Anak

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.182
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0130.002

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.

Opus teacher head0.051
GPT teacher head0.346
Teacher spread0.295 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations1
Published2018
Admission routes1
Has abstractyes

Explore more

Same topicPublic Health and NutritionFrench-language works237,207