Are Science Comics a Good Medium for Science Communication? The Case for Public Learning of Nanotechnology
Bibliographic record
Abstract
Comic books possessing the features of humour, narrative, and visual representation are deemed as a potential medium for science communication; however, empirical studies exploring the effects of comics are scarce. The purposes of this study were to examine and compare the impacts of a comic book and a text booklet on conveying the concepts of nanotechnology and to investigate public perceptions of using comics as a tool for science communication. A mixed-methods quasi-experimental design was used to explore these central issues. Three instruments were adopted to assess public knowledge of nanotechnology, public attitudes towards nanotechnology, and public emotional perceptions of learning science. Furthermore, 7 short-answer questions accompanying the posttest as well as interviews were administered to enrich the instrument results. The proportional stratified sampling method was used to recruit more than 300 adults as a pool of participants. Finally, the responses of 194 participants who completed the instruments were analysed. The results indicated that the comic book significantly promoted laypeople's knowledge of and attitudes towards nanotechnology as did the text booklet. It is noted that the comic book increased the participants' interest in and enjoyment of learning, while the text booklet decreased their interest and enjoyment. More comic readers were interested in learning nanotechnology via comics than text readers were interested in learning via text. Although there was no significant difference between the 2 media in the aspects of knowledge and attitude, the results of emotional perceptions imply that science comics have the potential to develop laypeople's ongoing interest and enjoyment for learning science by reading comics.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".