Strategic Study of On-Line Reading Clubs in Senior High School Libraries of Taiwan
Bibliographic record
Abstract
In this 21st century, information flow has already reached a matured level. It is the time to promote on-line reading clubs, as high school students generally possess the ability to study by themselves through the internet. In 1990, Taiwan Ministry of Education announced the plan to practice inter-schools on-line reading clubs for all high schools and constructed the students’ website to achieve a significant milestone for on-line reading clubs on campus. The on-line reading clubs are running through interactive web pages, electronic book stores, discussion boards, knowledge sharing etc. The essence of these clubs is the combination of key elements: reading, materials and communication. In this research, we first conduct with questionnaire and interview approaches to study their current situations, executive guidelines, resource requirements, performance evaluations and operating strategies of on-line reading clubs in Taiwan. Then we give a blueprint of well-function on-line reading club web environment to encourage students to involve in the clubs and cultivate inter-school reading. Finally, we propose strategic suggestions for practicing on-line reading clubs in senior high school libraries.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".