Bibliographic record
Abstract
Since 2009, Taiwan's Ministry of Education has begun to add the post of teacher librarians in some elementary schools. Teachers who are enthusiastic about reading education become teacher librarians to take charge of managing school libraries and promoting reading after receiving short professional training. This study aims to probe into whether the system is helpful to the management of libraries in elementary schools and to the establishment of reading environment at school. Specifically speaking, this study aims to discuss whether there is any difference in the activities of school libraries and schools' attitudes toward reading between schools with teacher librarians and schools without ones. The study surveyed elementary schools in Taiwan with a questionnaire which was filled out by teacher librarians or general librarians at school. The questionnaire was tested with Cronbach’s α reliability, and a coefficient of 0.975 was obtained, which is considered excellent reliability. 742 copies of the questionnaire were retrieved, and 741 of them were considered valid after the elimination of one with incomplete answers. Among which, 213 copies were from schools with teacher librarians, and the rest 528 copies were from schools without teacher librarians. In addition, the fill rate of schools with teacher librarians reached 80%. The study found that schools with and without teacher librarians had significant differences in library management of their libraries and behavior and attitudes toward reading.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.204 | 0.122 |
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".