Governing synthetic biology for global health through responsible research and innovation
Bibliographic record
Abstract
Synthetic biology (SynBio) is a global endeavour with research and development programs in many countries, and due (in part) to its multi-use characteristics it has potential to improve global health in the area of vaccine development, diagnostics, drug synthesis, and the detection and remediation of environmental toxins. However, SynBio will also concurrently require global governance. Here we present what we have learnt from the articles in this Special Issue, and the workshop we hosted in The Hague in February of 2012 on SynBio, global health, and global governance that generated many of the papers appearing here. Importantly we take the notion of 'responsible research and innovation' as a guiding perspective. In doing so our understanding of governance is one that shifts its focus from preventing risks and other potential negative implications, and instead is concerned with institutions and practices involved in the inclusive steering of science and technology towards socially desirable outcomes. We first provide a brief overview of the notion of global health, and SynBio's relation to global health issues. The core of the paper explores some of the dynamics involved in fostering SynBio's global health pursuits; paying particular attention to of intellectual property, incentives, and commercialization regimes. We then examines how DIYbio, Interactive Learning and Action, and road-mapping activities can be seen as positive and productive forms of governance that can lead to more inclusive SynBio global health research programs.
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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 | 0.036 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.036 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".