STRATEGI KEPEMIMPINAN KEPALA SEKOLAH DALAM MENINGKATKAN KINERJA GURU DI SMA KRAMAT DUKUPUNTANG KABUPATEN CIREBON
Bibliographic record
Abstract
This research is motivated by the principal's leadership strategy in improving teacher performance which has not been optimal. This is because the school principal only supervises once a year, does not provide motivation to teachers, and lacks infrastructure. Even though a school principal must have a strategy so that gruu performance can increase. \nThis research is a qualitative descriptive study. Data collection was carried out using in-depth interview techniques, observation and documentation. For data analysis using data reduction, data presentation and drawing conclusions. \nThe results of the study, 1) The principal's leadership strategy in improving teacher performance at Kramat Dukuntang High School by dividing teacher assignments, directing teachers to make lesson plans, increasing teacher capacity through the MGMP program, monitoring and evaluating teacher performance improvements. 2) Constraints that occur in the principal's leadership strategy in improving teacher performance at Kramat Dukuntang High School, some teachers are late for school, leave earlier than the specified time in ending the school teaching and learning process, are not motivated to improve performance, do not respond to exemplary leadership and supervision at Kramat Dukuntang High School it is held only once a year, there is a lack of school infrastructure, the superintendent has little role in coaching. 3) The way the principal overcomes obstacles in improving teacher performance at Kramat Dukuntang High School is to increase discipline, provide motivation in developing human resources, the principal supervises once a quarter and coordinates with related parties to complete learning infrastructure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 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) | 0.024 | 0.006 |
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".