Faktor-faktor yang Memengaruhi Efektivitas Sekolah dengan Kemandirian Sekolah sebagai Variabel Intervening menggunakan Pendekatan Partial Least Square
Bibliographic record
Abstract
The government encourages Vocational High Schools to carry out organizational transformation to become Regional Public Service Bodies. Through BLUD, SMKs with superior products can manage finances and production processes more flexibly without violating regulations so that school effectiveness can be achieved. This study aims to analyze the factors that influence SMK-BLUD schools' effectiveness through school independence using partial least squares structural equation modeling (PLS-SEM) analysis. The data were obtained by distributing questionnaires to 231 respondents from students, teachers, school principals, and committees in 25 SMK-BLUDs in East Java and DKI Jakarta. It is known that the variables focus on customers, focus on processes, and continuous improvement have a positive and significant impact on school effectiveness. In addition, the independence of each school indirectly also has a positive and significant effect on school effectiveness. The results of this modeling show the value of Predictive Relevance (Q2) of 0.965, meaning that this model has a good Predictive Relevance.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".