Bibliographic record
Abstract
Tulisan ini mendeskripsikan tentang Merdeka Belajar di Indonesia yang akan diterapkan pada 2021, namun belum tuntas karena penelitian terdahulu masih cenderung membahas bagian hilirnya dan belum menyentuh bagian hulu dari program perbaikan mutu pendidikan nasional itu. Data dalam tulisan ini berasal dari kajian pustaka buku/ tulisan ilmiah hasil penelitian tentang asessment kompetensi dan survey karakter, serta observasi lapangan pelaksanaan pendidikan madrasah di Wilayah Kemenag Prov. Sulawesi Tengah. Hasil penelitian menemukan bahwa: (1) Tenaga pendidik belum sepenuhnya memahami konsep merdeka belajar, bahkan cenderung salah paham (2). Tujuan merdeka belajar tidak sekedar mengganti Ujian Akhir Sekolah Berstandar nasional (UASBN) dan Ujian Nasional (UN) atau Rencana Pelaksanaan Pembelajaaran (RPP) dan Zonasi, namun dalam perspektif ke depan adalah untuk meningkatkan kualitas pendidikan nasional dan penyiapan SDM memasuki era Global, (3) Peningkatan kompetensi tenaga pendidik dan kependidikan merupakan kunci utama keberhasilan dari implementasi merdeka belajar. Untuk itu, tulisan ini menyarankan agar pemerintah dalam rentang waktu yang relatif pendek ini dapat mengalokasikan anggaran dan memprogramkan peningkatan kompetensi guru secara intens dan masif serta memfasilitasinya seiring semangat mengimplementasikan merdeka belajar.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 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".