How to teach life sciences students about dual-use research—a view from the field
Bibliographic record
Abstract
To reduce biological risks, raising awareness for dual-use issues already at the level of university education is essential. Currently, most life sciences education programs do not incorporate biosecurity and dual-use in their regular curricula. Consequently, the responsibility rests with individual lecturers and depends on their initiative to incorporate dual-use topics into teaching activities. Students interested in biosecurity and dual-use topics often only have the option to educate themselves in external or online courses. Here, we provide practical guidance on how to initiate and integrate a dual-use education program within the curriculum and provide a selection of existing teaching materials. In addition, we suggest key learning objectives to guide the planning of dual-use courses. Different course formats like lectures, seminars, or stand-alone events are discussed regarding their advantages, disadvantages, and suitability for conveying the learning objectives to different educational stages and audiences. As a minimum, we recommend the incorporation of dual-use issues into at least one mandatory course. Ideally, students should additionally participate in in-depth seminars, which can be voluntary and offered in cooperation with external organisations.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.016 | 0.011 |
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".