MANAJEMEN BIMBINGAN DAN KONSELING DI SMAN 4 YOGYAKARTA
Bibliographic record
Abstract
Tujuan penelitian ini adalah untuk mengungkap manajemen bimbingan dan konseling di SMAN 4 Yogyakarta. Penelitian ini menggunakan pendekatan kualitatif. Jenis penelitian adalah studi kasus. Pengumpulan data menggunakan teknik observasi, wawancara dan studi dokumen. Instrumen penelitian adalah peneliti sendiri. Analisis yang digunakan dalam penelitian ini adalah model analisis interaktif dari Miles dan Huberman melalui kegiatan pengumpulan data, reduksi data, penyajian data, dan penarikan kesimpulan. Hasil penelitian menunjukkan sebagai berikut: manajemen bimbingan dan konseling di SMAN 4 Yogyakarta terdiri atas perencanaan, pengorganisasian, pelaksanaan, dan pengawasan, belum semuanya dilakukan optimal. (1) Perencanaan program BK didasarkan pada analisis kebutuhan siswa, bersifat fleksibel, namun belum berdasarkan analisis lingkungan (2) Pengorganisasian BK, pembagian tugas sesuai dengan mekanisme namun terkendala waktu karena banyak tugas guru BK di luar BK, konselor dan konseli belum seimbang. (3) Pelaksanaan BK, belum menggunakan model BK komprehensif, beberapa layanan belum dilakukan optimal karena banyaknya tugas guru BK di luar kegiatan BK. (4) Pengawasan BK belum dilakukan optimal sebagaimana mestinya. Kata kunci: manajemen bimbingan dan konseling
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.048 | 0.007 |
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".