ANALISIS ANGGARAN RESPONSIF GENDER PADA APBD KABUPATEN SIDENRENG RAPPANG TAHUN 2019
Bibliographic record
Abstract
Pengarusutamaan gender merupakan salah satu agenda penting untuk mewujudkan pembangunan nasional yang lebih inklusif. Akan tetapi, fenomena di daerah menunjukkan bahwa pengarusutamaan gender belum memberi efek secara signifikan. Salah satu parameter yang dapat ditinjau yaitu berdasarkan alokasi anggaran pemerintah daerah. Penelitian ini bertujuan untuk menganalisis implementasi anggaran responsif gender pada pemerintah daerah periode APBD tahun 2019 dengan fokus lokasi penelitian di Kabupaten Sidenreng Rappang khususnya Dinas Pemberdayaan Masyarakat Desa, Perempuan, dan Perlindungan Anak. Metode penelitian yang digunakan yaitu kualitatif dengan analisis data interaktif Miles dan Huberman. Hasil penelitian mengindikasikan bahwa pemerintah daerah Kabupaten Sidenreng Rappang belum menerapkan pengarusutamaan gender dalam pembangunan sebab hasil evaluasi anggaran menunjukkan bahwa anggaran di Dinas Pemberdayaan Masyarakat Desa, Perempuan, dan Perlindungan Anak belum memenuhi aspek anggaran responsif gender. Hal tersebut dapat menjadi acuan informasi bagi pemerintah daerah untuk memperhatikan gender dalam mewujudkan pembangunan inklusif.
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 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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".