ANALISIS KEBUTUHAN SDM DAN SARANA PRASARANA SEKOLAH JENJANG PENDIDIKAN DASAR DAN MENENGAH PERTAMA DI KOTA PANGKALPINANG
Bibliographic record
Abstract
This study aims to analyze the suitability and fulfillment of infrastructure and human resource needs at the elementary and junior high school levels in Pangkalpinang City as a basis for regional education policy planning. The study used a qualitative approach with descriptive analytical methods. Data were collected through observation, surveys, structured interviews, and documentation. Data validity was ensured through triangulation of techniques and sources to verify findings in the field. The results showed that the condition of elementary and junior high school classrooms varied from decent to severely damaged. The needs analysis revealed the urgency of rehabilitation for classrooms with moderate to severe damage and the need to standardize supporting facilities such as lighting, prayer rooms, health facilities, and counseling rooms, which currently do not meet ideal criteria. In terms of human resources, an equitable distribution of teaching staff was identified to address the disparity in academic background and employment status, although teacher competency was generally categorized as good to excellent. The study concluded that fulfilling the gap between actual conditions and standard facility needs and the redistribution of human resources must be a top priority to improve the quality of education services in Pangkalpinang City.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".