Understanding a science-themed puppet theatre performance for public engagement in Thailand
Bibliographic record
Abstract
Background: Fishy Clouds, a 45-minute non-verbal touring puppet theatre show, was created with the objectives of (1) raising awareness of antimicrobial overuse and misuse (the fact that there simply is a problem), (2) raising awareness of the importance of research with children – including those involving antimicrobials, and (3) producing a science-themed performance of entertainment value and high artistic quality. The show used visual storytelling to bring the research and behaviour around antimicrobial resistance (AMR) to life for a broad range of audiences across different ages, locations, levels of education, and language. Methods: In order to understand the effectiveness of Fishy Clouds, we used a realist-informed evaluation approach. A combination of quantitative and qualitative approaches (semi-structured interviews, focus group discussions and field notes) were used for data collection. Results: We received a total of 880 quantitative feedback forms, conducted 22 semi-structured interviews and three focus group discussions. Our data showed that Fishy Clouds was an enjoyable performance to all audience groups and stakeholders and was generally viewed with artistic integrity. However, its effectiveness was primarily in raising existing awareness about medicine use and health more broadly, rather than specific health messaging concerning AMR and research with children. We found that those with limited background on AMR or research with children, such as school children and Karen ethnic migrants exhibited a wide range of interpretations. A science-themed theatre would function better if it is focussed on a single theme, embedded within a programme of activities and conducted at closed venues. Conclusions: Fishy Clouds showed that science theatre events have the potential to support public health programmes and engage local communities in science research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".