Bibliographic record
Abstract
This study aims to determine the concept of educational policy applied during the Old Order Period from17 August 1945 until before entering the New Order period. The method in this research emphasizes the type of library research. Obtained from books related to the main problem, namely the Old Order government policy in implementing the education system in Indonesia. The technique of collecting data is through books, journals, articles and magazines and the internet related to the Indonesian education system policy during the Old Order era. Data analysis used the analysis technique proposed by Miles and Huberman with the stages of data reduction, data presentation and conclusion (verification). The results of the study show that the implementation of the implementation of Indonesian education in the old order era has been fairly good, this can be shown by the development of Islamic education in tiered Madrasas at the Ibtidaiyah & Tsanawiyah level and the establishment of the Religious Teacher Education (PGA) and the State Islamic Judge Education (PHIN) madrasah. As for the application of the Islamic education system in public schools that have been going well. Public school conditions; Low Education, Teacher Education, Vocational Education & Technical Education.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".