Principal Procurement Policy Analysis in Achieving School Management Excellence
Bibliographic record
Abstract
This research of principal procurement policy focuses on organizing Principal Leadership Preparation Program (in Bahasa Indonesia is abbreviated as PPCKS) activities with participants coming from public elementary school teachers and state junior high school teachers. The research method is qualitative using case study approach with purposeful sampling. The implementation strategy of data collection through validity test, followed by triangulation of data in the form of interview, documentation study and observation. Data analysis used is data reduction, data presentation and data verification. The results of this study illustrate that: 1) the issue of education policy in the school principal procurement sometimes raises the dualism of interests normatively and politically; 2) the implementation of school principal procurement policy through PPCKS shown that it seems the committee from Educational Office element only role in the recruitment only, beyond that all controlled by LPPKS and Educational Office only did the monitoring; 3) most principals do not have PPCKS follow-up programs. Each principal should have a program and strategy in preparing candidates for the principal to be able to produce an effective principal.
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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 | 0.019 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".