Bibliographic record
Abstract
Perencanaan dalam dunia pendidikan, terutama dalam sebuah lembaga pendidikan,memang sangatlah penting, sebab perencanaan tersebut kedepannya akan berperanvital sebagai petunjuk dalam gerak langkah lembaga tersebut. Namun demikian,model perencanaan dalam sebuah lembaga pendidikan tentunya akan sangatberbeda dengan perencanaan dalam sebuah perusahaan. Perusahaan yang notabeneberorientasi profit, tentu saja ‘memproses’ benda mati, baik berupa barang maupunjasa. Di lain pihak, lembaga pendidikan, atau dapat disebut sebagai sekolah,‘memproses’ manusia dengan segala sifat-sifat kemanusiaannya yaitu hidup danberkembang. Perencanaan dalam sebuah lembaga pendidikan, tentunya tidak bolehkeluar dari tujuan pendidikan itu sendiri, karena tujuan itulah yang nantinya akanmenjadi titik tolak penyusunan sebuah kerangka rencana. Dan agar sebuahperencanaan dalam lembaga pendidikan tersebut tidak keluar dari tujuanpendidikan maka harus digunakan sebuah pendekatan, metode, dan teknik-teknikperencanaan yang sesuai dan tepat.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".