Synthetic biology in the Science Café: what have we learned about public engagement?
Bibliographic record
Abstract
Engaging the public on emerging science technologies has often presented challenges. People may hold notions that science is too complicated for them to understand and the venues at which science is discussed are formal and perceived as inaccessible. One approach to address these challenges is through the Science Café, or Café Scientifique. We conducted five Science Cafés across Canada to gauge public awareness of synthetic biology technology, its potential applications, and to evaluate the effectiveness of the Science Café platform as a knowledge-translation tool. Café participants were excited about the potential benefits of synthetic biology technology, but also concerned about the potential risks. And while participants trusted scientists to carry out their research, there was limited confidence that regulators would ensure public safety. Science Cafés as a forum for science to meet society were viewed positively for the relaxed atmosphere, small crowd size and informality of the venue. We conclude that Science Cafés are an effective upstream engagement platform for discussing emerging science technologies.
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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 | 0.050 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.019 | 0.027 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 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".