BAGAIMANA OPEN GOVERNMENT DITERAPKAN DALAM PERENCANAAN PEMBANGUNAN DAERAH? (Sebuah Analisis dengan Menggunakan Soft Systems Methodology)
Bibliographic record
Abstract
Studi ini bertujuan untuk menggambarkan aspek-aspek open government dalam perencanaan pembangunan daerah dan menganalisis perencanaan pembangunan yang ideal dalam mengadopsi aspek-aspek open government. Desain penelitian ini menggunakan pendekatan kualitatif dengan Soft Systems Methodology (SSM). Teknik analisis data dengan menggunakan analisis CATWOE (Customer, Actor, Transformation, Weltanschaung, Owner, dan Environment). Adapun hasil penelitian ini menunjukkan bahwa 1) Perencanaan pembangunan daerah sudah secara alamiah telah mengadopsi aspek-aspek dari OG yaitu partisipasi, trnasparansi, dan kolaborasi namun belum dilakukan secara sengaja (by design) sehingga penetapan program kegiatan dalam RAPBD tidak sesuai dengan hasil musrenbang; 2) Adanya jaminan transparansi dalam setiap proses perencanaan pembangunan dan maksimasi penggunaan TIK menjadi syarat diadopsinya aspek-aspek OG dalam perencanaan pembangunan yang ideal. Penelitian ini juga merekomendasikan dilakukannya penelitian khusus terkait OG dan pengayaan konsep partisipasi dan transparansi.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".