The Geographies and Politics of Gene Editing: Framing Debates Across Seven Countries
Bibliographic record
Abstract
This article traces the contours and dynamics of the debates about the politics of gene editing. It does so by providing both a quantitative and qualitative analysis of the publications on the topic. We present a scientometric analysis of scientific publications; we discuss the geographies of gene editing by analysing the scales and spatial terms mobilised; and we undertake a lexicometric analysis of how debates are framed and the public is positioned. Our scientometric analysis of scientific articles shows that the governance and regulation of gene editing is discussed across an increasing range of disciplines and countries over the years. Along with this internationalisation and “transdisciplinarisation,” we see a qualitative shift in the “grounding” of the debate: while initially, authors tend to reflect about gene editing, in more recent years, there are increasing calls to act upon current knowledge. Across the countries we studied (the United States, the United Kingdom, Germany, China, Australia, Japan, and Canada) our lexicometric analysis shows only a few differences in terms of how gene editing is discussed. While the general framing of the debate is widely shared, the differences that we observe concern for instance the applications or benefits of gene editing and the ways in which the importance of involving the public is worded. We hold that bringing together multiple methods allows a rich and multifaceted discussion of the politics of gene editing, and that this opens up fertile dialogues between geography, sociology and political science.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.022 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".