PENGELOMPOKKAN KABUPATEN/KOTA DI PROVINSI JAMBI BERDASARKAN KOMPONEN SEKTOR PENDIDIKAN
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Pendidikan memegang peranan yang sangat penting dalam meningkatkan sumber daya manusia..Pada masa pandemi Covid-19 seperti saat ini, dunia pendidikan tengah berada dalam kekacauan. Kualitas pendidikan di suatu wilayah dapat dilihat dari nilai Angka Partisipasi Sekolah (APS), Angka Partisipasi Murni (APM), Angka Partisipasi Kasar (APK). Nilai-nilai tersebut juga merupakan indikator tercapainya pembangunan dalam bidang pendidikan di suatu wilayah. Metode yang digunakan pada penelitian kali ini adalah metode single lingkage. Berdasarkan algoritma cluster yang dilakukan diperoleh hasil bahwa dari 11 Kabupaten/Kota di Provinsi Jambi terkelompok 4 cluster yaitu cluster 1 terdiri dari Kerinci dan Kota Sungai Penuh, cluster 2 terdiri dari Merangin, Tanjung Jabung Barat, Bungo dan Tebo, cluster 3 terdiri dari Sarolangun, Batanghari, Tanjung Jabung Timur dan Muaro Jambi dan cluster 4 terdiri dari Kota Jambi. Keempat cluster tersebut diurutkan berdasarkan tingkatannya yaitu cluster 1 sangat baik, cluster 2 cukup baik, cluster 3 baik dan cluster 4 lebih baik.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it