Policy Recommendations for Learning Encouragement of District Learning Encouragement Centers Under the Department of Learning Encouragement
Bibliographic record
Abstract
The research aimed to introduce policy proposals for learning promotion at the District Learning Encouragement Centers under the Department of Learning Encouragement. The research consisted of three phases: Phase 1 involved studying the current situation, desired conditions, and necessary needs to draft policy proposals for promoting learning at the District Learning Encouragement Centers. The sample consisted of 1,368 administrators and teachers from the Department of Learning Encouragement, selected through multi-stage random sampling using stratified sampling principles, and studying three cases. The target group included nine school administrators and teachers chosen through purposive selection. Phase 2 focused on drafting policy proposals for promoting learning at the District Learning Encouragement Centers through expert-based seminars. The target group consisted of 11 selected experts. Phase 3 involved reviewing and holding public consultations on the policy proposals for promoting learning at the District Learning Encouragement Centers. The target group consisted of 39 administrators, educational supervisors, and teachers selected through purposive selection. The research tools included questionnaires, interviews, and evaluation forms. The statistical methods were percentages, means, standard deviations, and content analysis. The research results indicated that the policy proposals for promoting learning at the District Learning Encouragement Centers included five key policy areas: vision promotion, lifelong learning promotion, social and community development, qualification-based learning promotion, and diverse learning promotion. The evaluation of the policy proposals for promoting learning at the District Learning Encouragement Centers overall indicated a high level of appropriateness, feasibility, and overall benefit across all aspects.
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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 | 0.052 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.014 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".