PENGARUH IKLIM ORGANISASI TERHADAP KOMITMEN GURU
Bibliographic record
Abstract
Komitmen guru merupakan elemen penting yang berkontribusi pada efektivitas sekolah. Penelitian ini bertujuan untuk mengetahui hubungan antara iklim sekolah dengan komitmen guru dan dampak dari iklim sekolah yaitu kepemimpinan kolegial, prestasi akademik, profesionalisme guru dan kerentanan kelembagaan terhadap komitmen guru. Faktor-faktor tersebut berhubungan langsung dengan efisiensi dan efektivitas sekolah. Organizational Climate Index yang dikembangkan oleh Hoy, Smith, dan Sweetland dan Organizational Commitment Questionnaire yang dikembangkan oleh Mowday, Steers, dan Porter digunakan sebagai instrumen survei. Data dikumpulkan dari 42 guru SMPN 9 Padang untuk penelitian ini. Hasil penelitian menunjukkan adanya hubungan antara iklim sekolah dengan komitmen guru. Hasil analisis regresi menunjukkan bahwa kepemimpinan kolegial dan kerentanan kelembagaan merupakan prediktor komitmen guru. Temuan penelitian ini dapat berkontribusi untuk mengembangkan wawasan bagi administrator sekolah dan kepala sekolah untuk membuat intervensi yang diperlukan untuk mengembangkan iklim sekolah yang positif.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.144 | 0.023 |
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".