TAHAP-TAHAP PENYUSUNAN MODUL AJAR KURIKULUM MERDEKA TINGKAT SEKOLAH
Bibliographic record
Abstract
Kurikulum Merdeka is initiated by Nadiem Makarim, the Minister of Education, aiming to return the authority of educational management to local governments and schools in accordance with local’s needs, capacities, and wisdom. Even though this curriculum was officially launched in February 2022, until now, teachers in Indonesia, especially teachers at SD Negeri Pertiwi, Lamgarot Aceh Besar, are still unfamiliar, especially with regard to teaching modules based on this Kurikulum Merdeka. Therefore, lecturers from several universities in Aceh were inspired to assist these teachers through a socialization and teach the teachers about the stages of preparing teaching modules. This activity aims to increase teachers’ capacity in compiling independent curriculum teaching modules so that they can focus on being learning facilitators. The method used was socialization through lectures. The results of this activity indicated that there was an increase in the knowledge and skills of SD Negeri Pertiwi, Lamgarot, Aceh Besar teachers in compiling teaching modules based on Kurikulum Merdeka. Keywords: Kurikulum Merdeka, teaching module, socialization
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".