A Framework for Understanding the Nature of Questions Asked by Audience Participants at Science Cafés
Bibliographic record
Abstract
Science Cafés are events designed as public engagement tools that create the opportunity for open dialogue between members of the general public and experts on the issue being discussed at the event. This study explores the nature of questions being asked by audience participants during discussion sessions of Science Café events. It was conducted by coding audio recordings of audience participant engagements at 41 Science Café events held between 2010 and 2019. The result of this analysis produced a novel taxonomic framework to describe audience participant behaviors in terms of their learning goals. This framework was evaluated by applying it to samples of Science Café question data selected by Science Café topic theme. Comparisons between question-asking behaviors for specific Science Café topics and overall trends in question-asking behaviors for all Science Cafés revealed significant changes in audience participant learning goals when asking questions at Science Cafés centered on different topic types. Implications for understanding Science Café audiences and potential developments for Science Café events as public science engagement tools are discussed.
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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 | 0.035 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.005 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".