Librarians’ perceptions of educational values of comic books: A comparative study between Hong Kong, Taiwan, Japan, Australia and New Zealand
Bibliographic record
Abstract
Comic books are becoming increasingly popular in the field of education. In the past, comic books were excluded from school libraries and classrooms. However, with the resurgence in the popularity of comic books and students’ increased demands for them, they are now considered as recreational reading with educational value. In response to this, school libraries have begun collecting comic books and including them as part of their regular collections. This research paper reflects on the current situation of comic books in primary and middle school library collections and examines school librarians’ perceptions towards educational values of comics. The investigation was launched in Hong Kong, Taiwan, New Zealand, Australia and Japan – making comparisons amongst different levels (primary school and secondary school), and different types (public school and private school) of schools in five different countries. Questionnaire surveys were sent to selected school librarians and were the main method of data collection. A total number of 683 responses were collected for this study. Research results include librarians’ attitudes towards comic books in school libraries, adolescent readers’ use of school libraries, their reading and borrowing practices, as well as other problems encountered with the on-going maintenance of comic books as part of the school libraries’ regular collections.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".