Strategies for Developing Quality Community Schools under the Office of the Basic Education Commission
Bibliographic record
Abstract
The purposes of this research aimed to develop and validate the suitability, possibility and benefits of strategies implementation, and create a user manual of the strategies of quality community schools under the Office of the Basic Education Commission. This study was conducted in five phases. The first phase was the intensive review of the components of quality community schools using relevant documents, research analysis and multiple case studies of the outstanding quality community schools, and an interview with experts. The second phase was the exploration of the needs of the development of quality community schools under the Office of the Basic Education Commission using a 5-rating scale questionnaire to explore the current condition and desirable condition of 192 administrators and the head of academic affairs department of schools under the Office of the Basic Education Commission. The participants were selected using multi-stage sampling. The third phase was the development of the quality community school development strategies through focus group discussion of nine qualified experts and scholars. The fourth phase was the validation of suitability, possibility and benefits of the quality community school development strategies by 7 participants of experts and stakeholders of the quality community schools. And the last phase was creating and validating the user manual of the strategies implementation. Statistics used in data analysis were mean, standard deviation, percentage and Priority Needs Index (PNImodified).
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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 | 0.033 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".