The Impact of the Principal's Servant Leadership on Teacher Performance at SMP Negeri 4 Laguboti in 2023
Bibliographic record
Abstract
Abstract: The objective of this study is to determine the influence of the principal's servant leadership on teacher performance at SMP Negeri 4 Laguboti. The research employs a quantitative inferential statistical method. The population consists of 32 teachers at SMP Negeri 4 Laguboti, with a sample size of 30 teachers selected from SMP Negeri 3 Lintongnihuta for the pilot test. Data were collected using a closed-ended questionnaire comprising 60 items. The data analysis results indicate a significant impact of the principal's servant leadership on teacher performance at SMP Negeri 4 Laguboti. The analysis of the relationship between variables X and Y yielded an rxy value of 0.527, which is greater than the critical value of rtabel(α=0.05,n=32) = 0.349, and a t-value of 3.394, exceeding the critical t-value of 1.684, indicating a positive and significant relationship between the variables. The simple linear regression equation obtained is ŷ = 33.949 + 0.735X. Hypothesis testing showed that Fhitung = 11.518, which is greater than Ftabel = 4.08, leading to the rejection of the null hypothesis (Ho) and acceptance of the alternative hypothesis (Ha). The study concludes that there is a positive and significant effect of the principal's servant leadership on teacher performance at SMP Negeri 4 Laguboti, with the coefficient of determination (r²) = 27.7%.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".