Governing synthetic biology for global health through responsible research and innovation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it