Organizational change of synthetic biology research: Emerging initiatives advancing a bottom-up approach
Bibliographic record
Abstract
Bottom-up Synthetic Biology (buSynBio) is an approach focused on the artificial making of minimal functional biosynthetic systems by recombining existent biochemical modules or manufacturing them from scratch. Over the last decade, this emerging orientation has gained new momentum with the development of new bioengineering tools, theories, and technologies. Despite the growing acceptance of buSynBio, few studies have dedicated attention to the analysis of its social and institutional aspects. This article offers the first systematic investigation of emerging research initiatives in buSynBio and their meaning to the present shape of bioengineering research. Our analysis is based on a multi-method qualitative study, including expert literature review, bibliometric research and a documentary analysis of online materials as reports and project descriptions available in official grant data repositories. Our study found that publications of specialized articles on “bottom-up synthetic biology” have increased both in absolute numbers and normalized to total number of publications. We show how that might be enabled by novel mechanisms of scientific organization that reposition the material, intellectual and political resources of synthetic biology. Drawing on theoretical analyses of knowledge infrastructures within Science and Technology Studies (STS), we examine 14 research initiatives in 5 selected countries (Germany, United Kingdom, United States, Netherlands, and Switzerland). The bottom-up approach is supported by a variety of “tentative regimes” of scientific governance in different stages of consolidation, but holding in common the establishment of basic in Chemistry, Biology, Engineering and Physics. The study aims to contribute to social science research in synthetic biology by shedding light on the implications of buSynBio as a novel trend driving present and future organizational change of bioengineering research.
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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 | 0.045 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.008 | 0.032 |
| Scholarly communication | 0.023 | 0.016 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".