Pelatihan Manajemen dan Penanganan Kasus Perempuan dan Anak di Kota Bima Nusa Tenggara Barat
Bibliographic record
Abstract
Tujuan pelatihan manajemen dan penanganan kasus perempuan dan anak antara lain: (a) Peserta diharapkan mampu mengenali dan memahami dalam menangani masalah perempuan dan anak yang terjadi di tengah masyarakat. (b) Peserta dapat mengsimulasi dalam menangani kasus perempuan dan anak melalui sketsa gambar dan deskriptif, (c) Peserta dapat mengimplementasikan alur manajemen kasus perempuan dan anak, (d) Peserta dapat menganalisis policy output dalam penanganan kasus perempuan dan anak, (e) Peserta mampu mendeskripsikan alur manajemen kasus dan policy output dalam penanganan kasus perempuan dan anak di Kota Bima Nusa Tenggara Barat. Metode pelatihan manajemen dan penanganan kasus untuk perlindungan perempuan dan anak antara lain: perjalinan relasi (engagement), penilaian (assessment), rencana (planning), intervensi (intervetion), pemantauan (monitoring), evaluasi (evaluation), terminasi (termination), dan tindak lanjut (follow-up). Peserta 35 orang dibagi ke dalam 5 kelompok dan menangani kasus perempuan dan anak yang berbeda-beda seperti kakerasan terhadap perempuan (KTP), kekerasan terhadap anak (KTA), Tindakan Pidana Perdagangan Orang (TPPO), Anak Berhadapan dengan Hukum (ABH), dan Pernikahan Usia Anak. Peserta dapat mengilustrasikan dan mendekripsikan kasus perempuan dan anak di masyarakan menggunakan simulasi gambar. Peserta mengilustrasikan dan mempresentasikan manajemen dan penanganan kasus perempuan dan anak.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.055 | 0.007 |
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".