The knowledge politics of genome editing in Africa
Bibliographic record
Abstract
How is the promise of crop genome editing viewed by scientists working with or aspiring to work with the technology, by development experts seeking to mold public perceptions and policy attitudes toward genome editing, and by donors that provide funds for genome-editing research for agricultural applications in sub-Saharan Africa? In this article, we present data from interviews with these stakeholders to shed light on their aspirations, concerns, and expectations. Previous scholarship on genome editing in relation to African agriculture has focused on the technical capabilities of genome editing techniques and surveys of current research and development activities in this field. This article contextualizes and reflects critically on expectations that genome editing can or will deliver benefits for African scientists and farmers. The interviews reveal excitement around genome editing and anticipation for what it could achieve, but also a sober realism and frustration regarding the political-economic hurdles that constrain African scientists and research institutions and the generation of public goods for African farmers and societies. These insights, we show, challenge extant narratives related to genome editing and accessibility. As such, we center and interrogate the politics of knowledge surrounding the emergence of genome editing in Africa.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.025 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".