Bridging the gap: enhancing science communication in synthetic biology with specific teaching modules, school laboratories, performance and theater
Bibliographic record
Abstract
Synthetic biology, an interdisciplinary field merging biology, engineering, and computer science, holds significant promise but also gives rise to ethical and safety issues and concerns. Effective communication of scientific concepts is essential to bridge the gap between the scientific community and the general public. Here we present four communication strategies from our own experience that could help address this gap: (i) the teaching module “iGEM-Synthetic Biology”: Students at Technische Universität Berlin (TU Berlin) engage in synthetic biology projects, showcasing their work in a competition format that includes the creation of videos and websites. (ii) Long Night of the Sciences: Students and supervisors from the international Genetically Engineered Machine (iGEM) module at TU Berlin share their results with presentations, experiments, and quiz games during this public event. (iii) Theatre play: Festival für Freunde e.V. has developed a play titled “Life from the Toolbox” to explain genetic modification and synthetic biology for the audience. The play incorporates readings and educational videos. (iv) Heinz-Bethge-Foundation electron microscopy school lab: This laboratory utilizes hands-on experiments with microscopes, including electron microscopes, to visually explain intricate scientific concepts in physics, biology, and synthetic biology. It encourages high school students to delve deeper into the realm of science. These four initiatives represent a communication strategy that resonates with diverse audiences and is suitable to cover the public as a target group independent from their prior knowledge of the scientific background.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.009 |
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".