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IMPLEMENTASI KEBIJAKAN PERENCANAAN DAN PENGANGGARAN YANG RESPONSIF GENDER (PPRG) KABUPATEN SINJAI

2023· article· id· W4383645791 on OpenAlexaff
Abd. Wahid, Suardi Mukhlis, Nirwana Nirwana

Bibliographic record

VenueJurnal Ilmiah Administrasita · 2023
Typearticle
Languageid
FieldSocial Sciences
TopicLocal Governance and Development
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsHumanitiesPhysicsPolitical scienceArt

Abstract

fetched live from OpenAlex

Penelitian ini bertujuan untuk mengetahui implementasi kebijakan Implementasi Kebijakan Perencanaan dan Penganggaran yang Responsif Gender (PPRG) pada Dinas Pemberdayaan Perempuan, Perlindungan Anak, Pengendalian Penduduk dan Keluarga Berencana Kabupaten Sinjai. Jenis penelitian ini adalah deskriptif dengan metode kualitatif. Informan penelitian adalah para pelaksana kebijakan di Dinas Dinas Pemberdayaan Perempuan, Perlindungan Anak, Pengendalian Penduduk dan Keluarga Berencana Kabupaten Sinjai. Metode pengumpulan data terdiri dari observasi partisipan, wawancara mendalam, dokumentasi dan triangulasi, dan teknik analisis data meliputi reduksi data, penyajian data dan verifikasi kesimpulan. Pedoman utama sebagai teori analitik adalah model implementasi kebijakan yang dikembangkan oleh Van Metter dan Van Horn yang terdiri dari aspek aspek ukuran dan tujuan kebijakan, sumber daya, karakteristik lembaga pelaksana, kecenderungan sikap (disposisi) pelaksana, komunikasi antara organisasi dan kegiatan serta ekonomi, sosial dan politik. Hasil penelitian menunjukkan bahwa implementasi kebijakan Perencanaan dan Penganggaran Responsif Gender telah dilaksanakan, namun belum maksimal karena sumber daya, Sikap/kecenderungan, serta karakteristik agen pelaksana kurang memahami kebijakan ini, serta komunikasi antara organisasi masih kurang dalam hal ini sosialisasi, ditambah dengan belum seluruh pejabat/staf di OPD yang memahami serta mengetahui tentang kebijakan tersebut karena koordinasi yang terbatas sehingga membuat kebijakan tersebut menjadi kurang konsisten dalam pelaksanaannya di lapangan, sedangkan ukuran dan aspek tujuan kebijakan, kecenderungan sikap (disposisi) para pelaksana serta ekonomi, sosial dan politik sudah berjalan secara optimal.

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)0.007
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.040
Threshold uncertainty score0.132

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0070.007
Open science0.0020.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0400.013

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 head0.078
GPT teacher head0.358
Teacher spread0.280 · 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 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".

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Citations3
Published2023
Admission routes1
Has abstractyes

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