The Influence of Headmaster Leadership Style on Teacher Performance
Bibliographic record
Abstract
The headmaster as a leader in a school has a very important role. This study aims to determine the effect of the headmaster leadership style on teacher performance at State Vocational School 1 Gowa. This research is quantitative research. Respondents were all teachers of State Vocational School 1 Gowa. Data collection techniques used through observation, questionnaires (questionnaires), interviews and documentation. Data obtained from the results of the study were processed using data analysis techniques consisting of descriptive analysis techniques and inferential statistical analysis. The results showed that the influence of the leadership style of the principal of the State Vocational School 1 Gowa was in a very good category with a percentage rate of 84.03 percent, and for the teacher performance of the State Vocational School 1 Gowa included in the very good category with a percentage rate of 84.49 percent. This can be proven by the results of the moment product correlation analysis of 0.280, which means the level of relations in the low category. It can be concluded that there is a Positive and Significant Influence of the influence of headmaster leadership style on the performance of teachers at State Vocational School 1 Gowa, seen from several indicators of leadership variables, namely directing style coaching style, and supporting style. Teacher performance variables, namely as learning designers, as instructors of learning, as managers of learning, as counselors, as evaluators, and as implementers of the curriculum.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".