Critical Geographies of Biotechnology Governance: A Case Study of Genetically Modified Mosquitoes for Vector-Borne Disease Control
Bibliographic record
Abstract
Biotechnology governance has garnered significant attention in the last decade. One notable example is the use of genetically modified mosquitoes (GMMs) to control vector-borne diseases (VBDs) such as malaria and dengue fever. While promising, GMMs are controversial. Some scientists and public health agencies support GMMs, however not all groups welcome biotechnology as a disease control measure. To date, most field trials have been met with controversial reception. While extensive literature addresses GMM governance, gaps remain including attention to geographical scale and scientific experts’ perspectives on engagement in governance processes. Considering these gaps, the objective of this thesis is to examine academic debates in GMM governance. Using methods such as a scoping review, and semi-structured qualitative interviews (n = 14), this thesis asks two interrelated questions. Drawing on critical geographies of scale, the first question asks: How is scale represented in the academic literature on GMM governance, and what power dynamics do representations of scale uphold or challenge? Drawing on theories of public engagement in science and technology studies (STS), the second question asks: What perspectives do scientific experts hold regarding public engagement for GMMs? First, key findings reveal that in the academic literature, GMM governance is largely framed through global/local scalar binaries which run the risk of perpetuating historical inequalities between regions and groups in ways that limit the potential for democratic engagement. Second, key findings spotlight how the values and visions of scientific experts, who are at the forefront of GMM development, play a central role in how public engagement unfolds. While participants valued the principle of engagement as an integral part of ethical research, most did not consider integrating engagement into research agenda-setting processes beyond GMM field trials. A critical geography lens provides an opportunity to examine how ideas and values around GMMs are embedded in sociocultural contexts. This research is timely given that these applications are in the early stages of development and oversight frameworks are evolving.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.020 | 0.017 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".