Effect of Principal Managerial Leadership and Compensation towards Physics Teacher Performance in Senior High School in Baguala District-Ambon
Bibliographic record
Abstract
The performance of teachers is an important factor that must be considered in efforts to improve the quality of education. Teacher’s performance is affected by many factors. Factors that affect the performance of teachers are principals’ managerial leadership and compensation. The purpose of this study was to determine the effect of principals’ managerial leadership and compensation to the performance of physical science teachers in senior high schools in the district Baguala Ambon city. This type of research is quantitative design. The instrument was used questionnaire. The variables studied were the principal managerial leadership (X1), compensation (X2) as independent variables and performance of science physics teachers (Y) as dependent variable. Prior to use in the true research, the validity and reliability of these instrument was done. To reveal relationships between variables, correlation analysis was performed, while to know the effect of independent variables on the dependent variable regression analysis also performed. Analysis of the validity, reliability, correlation and regression analysis was performed using SPSS program version of 18.0 for windows. The results showed that between principal managerial leadership and teacher performance there is a very high correlation. Between compensation with the performance have high correlation and the combination of principals’ managerial leadership and compensation with the performance there is a very high correlation. Regression analysis showed that the independent variables principals managerial leadership and compensation, or a combination of both contribute to or affect the performance of physical science teacher at senior high school in the district Baguala.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".