PENGEMBANGAN BAHAN AJAR TEMATIK BERBASIS KEARIFAN LOKAL KALIMANTAN BARAT PADA KELAS III TEMA 5 DAN 6 SD/MI
Bibliographic record
Abstract
Penelitian ini dilakukan untuk mengetahui pengembangan bahan ajar tematik berbasis kearifan lokal Kalimantan Barat pada Kelas III Tema 5 dan 6 Sekolah Dasar/Madrasah Ibtidaiyah. Untuk memudahkan peneliti mengambil data, maka peneliti menjabarkan tujuan khusus yaitu mengetahui aspek isi materi, aspek penyajian materi, aspek desain dan aspek bahasa dalam proses pengembangan bahan ajar berbasis kearifan lokal Kalimantan Barat. Penelitian menggunakan jenis penelitian R&D (Research And Development) dengan menggunakan model 4-D (Define,Design,Develov,Dessiminate). Selain itu, peneliti juga menggunakan pendekatan kualitatif untuk data-data penelitian yang bersifat kualitatif. Sumber data dalam penelitian ini yaitu hasil penilaian validator terkait tentang penilaian validasi isi materi, validasi penyajian materi, validasi desain dan validasi bahasa. Hasil validasi data dapat disimpulkan bahwa: Validasi bahan ajar tematik berbasis kearifan lokal Kalimantan Barat pada kelas III tema 5 dan 6 Sekolah Dasar/Madrasah Ibtidaiyah oleh enam validator. 1) Validator isi materi berada pada kriteria “Sangat Validan dan Tidak Revisi” dengan persentase sebesar 88,34%. 2) Validasi oleh validator penyajian materi berada pada kriteria “Sangat Valid dan Tidak Revisi” dengan persentase 97,14%. 3) Validasi oleh validator desain buku berada pada kriteria “Sangat Valid dan Tidak Revisi” dengan persentase 99,61%. 4) Validasi oleh validator bahasa berada pada kriteria “Sangat Valid dan Tidak Revisi” dengan persentase 90%.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.051 | 0.021 |
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".