PENGARUH GAYA KEPEMIMPINAN MUTU DAN DISTRIBUTIF KEPALA SEKOLAH DALAM MENINGKATKAN MUTU PENDIDIKAN MELALUI 8 STANDAR NASIONAL PENDIDIDKAN (SNP)
Bibliographic record
Abstract
Kajian ini bertujuan untuk: (1) mengetahui sejauhmana pengaruh kepemimpinan mutu kepala SMKN 2 Depok Sleman dalam meningkatkan mutu pendidikan melalui 8 Standar Nasional Pendidikan (SNP); (2) sejauhmana pengaruh kepemimpinan distributif kepala SMKN 2 Pengasih Kulon Progo dalam meningkatkan mutu pendidikan melalui 8 Standar Nasional Pendidikan (SNP). Kajian ini merupakan kajian kuantitatif. Populasi kajian adalah seluruh guru (termasuk kepala sekolah) yang mengajar di SMKN 2 Depok Sleman dan SMKN 2 Pengasih Kulon Progo di Provinsi D.I Yogyakarta. Sampel penelitian dipilih 30 Guru (termasuk kepala sekolah) berjumlah 30 masing-masing sekolah, sehingga total sampel berjumlah 60 orang. Pengumpulan data menggunakan angket dimana untuk menambahan informasi menggunakan observasi dan studi dokumentasi. Analisis data menggunakan teknik statistik kuantitatif dengan terlebih dahulu data diuji normalitas, linieritas dan multikolinieritas kemudian baru dapat dilanjutkan dengan analisis regresi tunggal. Hasil kajian adalah sebagai berikut. (1) Kepemimpinan mutu (X1) Kepala SMKN 2 Depok tidak berpengaruh terhadap SNP (Y) dengan nilai Sig. 0,543 (syarat berpengaruh nilai sig. < 0, 05); (2) Kepemimpinan distributif (X2) Kepala SMKN 2 Pengasih berpengaruh terhadap SNP (Y) dengan nilai Sig. 0,004 (syarat berpengaruh nilai sig. < 0, 05).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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 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".