PENERAPAN PENDEKATAN BCCT DI KELAS APEL (TK A) PAUD KASIH IBU KECAMATAN PONTIANAK TENGGARA (APPLICATION BCCT APPROACH AT APPLE CLASS (TK A), PAUD KASIH IBU, SUBDISTRICT OF SOUTHEAST PONTIANAK)
Bibliographic record
Abstract
Penelitian ini berjudul “Penerapan Pendekatan BCCT di Kelas Apel (TK A) PAUD Kasih Ibu Kecamatan Pontianak Tenggara yang bertujuan untuk mengetahui: (1) Perencanaan; (2) Pelaksanaan; (3) Penilaian,pada pendekatan pembelajaran tersebut. Sasaran penelitian ini adalah mendapatkan informasi yang tepatdan lengkap dari PAUD Kasih Ibu agar berguna bagi akademisi, praktisi, dan masyarakat luas, khususnyabagi bidang PAUD untuk usia 4-5 tahun. Metode penelitian ini adalah kualitatif dengan deskripsi analitikmelalui wawancara terstruktur, observasi, dan dokumentasi. Proses pembelajarannya meliputi empatpijakan, yaitu: Lingkungan main, sebelum main, saat main, dan setelah main. Ada empat sumberpengumpul data dalam penelitian ini: (1) Dua orang guru Kelas Apel; (2) Kepala Sekolah PAUD KasihIbu; dan (3) Sekretaris yang juga sebagai anggota pengelola PAUD Kasih Ibu. Hasil penelitiannyamenunjukkan kegiatan perencanaan, pelaksanaan, dan penilaian penerapan pendekatan BCCT di KelasApel (TK A) PAUD Kasih Ibu adalah “Cukup baik”. Analisis perencanaan, pelaksanaan, dan penilaiandifokuskan pada Rencana Pelaksanaan Pembelajaran Harian (RPPH) yang dibuat oleh guru-guru KelasApel PAUD Kasih Ibu dari bulan September 2015 sampai dengan bulan November 2015. Kata Kunci: Pendidikan Anak Usia Dini (PAUD), Pendekatan BCCT, RPPH
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.050 | 0.014 |
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".