Change and Dilemma of School Feature Development of Three Junior High Schools in the Remote and Rural Areas of Taiwan
Bibliographic record
Abstract
This research is based on qualitative approach and applies in-depth interview with three principals and administrators in three junior high schools located in the remote and rural areas of Taiwan. The aim of this paper was to explore the school feature development process in these three schools. The findings of this study were as follows: most of students’ parents in these three remote and rural schools are labors and have relatively lower social and economic status in the Taiwanese society. School education becomes an important way for these students to develop their academic, cultural, and technical competences. Second, most of the students’ learning motivation and academic performance were not well, but good at athletics or vocational skill. Besides, most of the features created by the schools would not last due to the un-stabilization and away of teachers, short of financial support, and lack of favor from community. Fourth, an important reason of developing school features of these three schools is out of the competition between the urban schools. The decline of the birth rate in the whole society of Taiwan also facilitates the motivation. Fifth, parents of these three schools do not support students’ participation in local cultural or local career related curriculum. Parents would have a high expectation on school education’s effects on their children’s future competitiveness.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".