Bibliographic record
Abstract
Konsep Manajemen Berbasis Sekolah (MBS) telahdisosialisasikan kepada semua pemangku kepentinganpendidikan sekolah (stakeholder) melalui berbagai kegiatan pelatihan (training). Pelatihan capacity building, sebagai contoh, telah dilaksanakan oleh beberapa lembaga bantuan asing yang bergerak di Indonesia. Lembaga bantuan asing tersebut antara lain adalah USAID dalam program Managing Basic Education (MBE), telah meluncurkan tiga materi utama dalam pelatihan, yaitu: (1) PAKEM (Pembelajaran Aktif, Kreatif, Efektif, dan Menyenangkan, (2) MBS (Manajemen Berbasis Sekolah, dan (3) PSM (Peran Serta Masyarakat). Trilogi materi pelatihan tersebut merupakan tiga materi utama pelatihan di samping materi pelatihan lainnya. Trilogi materi pelatihan tersebut mulai disosialisasikan terutama pada sekitar tahun 1990-an. Dalam periode ini, MBS juga mulai diadopsi oleh beberapa lembaga pelatihan yang dibiayai oleh lembaga donor internasional lain, misalnya ADB (Asian DevelopmentBank) dan AUSAID dengan program AIBEP (Autralia-Indonesia Basic Education Program), dan kemudian mulai diadaptasi dan dimasukkan ke dalam pelbagai kegiatan pelatihan yang dilaksanakan oleh Departemen Pendidikan Nasional, seperti dalam proses penyusunan Rencana Strategis Departemen Pendidikan Nasional 2005 – 2009.Jika pada periode Renstra Depdiknas 2005 – 2009 konsepMBS lebih dititikberatkan pada aspek terorinya, makadalam pelatihan berikutnya akan lebih ditekankan padaaspek penerapannya.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.006 | 0.001 |
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".