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Record W4391888490 · doi:10.1016/j.crbiot.2024.100188

Organizational change of synthetic biology research: Emerging initiatives advancing a bottom-up approach

2024· article· en· W4391888490 on OpenAlexaff
Renan Gonçalves Leonel da Silva, Jakob Schweizer, Kalina Kamenova, Larry Au, Alessandro Blasimme, Effy Vayena

Bibliographic record

VenueCurrent Research in Biotechnology · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGene Regulatory Network Analysis
Canadian institutionsOntario Genomics
FundersEidgenössische Technische Hochschule ZürichSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsOrganizational changeEngineering ethicsProcess managementEngineeringPolitical sciencePublic relations

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.045
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.992
Threshold uncertainty score0.236

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0450.038
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0100.010
Science and technology studies0.0080.032
Scholarly communication0.0230.016
Open science0.0020.015
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.134
GPT teacher head0.423
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

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