PENCAPAIAN TUJUAN PROGRAM KAMPUNG TEMATIK BERBASIS PENGARUSUTAMAAN GENDER DI KAMPUNG SENTRA BANDENG
Bibliographic record
Abstract
Saat ini Kota Semarang menghadapi permasalahan kawasan kumuh (112,49 Ha) dan pengentasan kemiskinan (4,14%). Program kampung tematik bertujuan meningkatkan kualitas fisik lingkungan kumuh dan mengurangi penduduk miskin. Pelaksanaan program kampung tematik sebaiknya menerapkan strategi pengarusutamaan gender, sesuai dengan Instruksi Presiden Nomor 9 tahun 2000 tentang Pengarusutamaan Gender dalam Pembangunan Nasional. Artikel ini menjelaskan apakah pelaksanaan program kampung tematik telah menerapkan pengarusutamaan gender. Penelitian dilakukan secara kuantitatif dengan teknik analisis skoring dan deskriptif. Indikator pengarusutamaan gender yang digunakan yaitu akses, partisipasi, manfaat, dan kontrol. Hasil analisis menemukan bahwa skor pencapaian tujuan program cukup baik (1,89 atau setara dengan 63%). Maka, dapat disimpulkan tujuan pelaksanaan program yang sudah tercapai yaitu peningkatan kualitas fisik dan yang belum tercapai adalah pengentasan kemiskinan. Hal ini dikarenakan bantuan sumber daya yang diperoleh peserta program belum merata dan tidak terdapat tempat pemasaran bersama sehingga peserta sulit mengembangkan usaha pengolahan bandeng.
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 | 0.001 | 0.002 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.048 | 0.005 |
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".