Does “Precision” Matter? A Q Study of Public Interpretations of Gene Editing in Agriculture
Bibliographic record
Abstract
Gene editing (GE) technologies are rapidly gaining traction as an alternative to genetically modified organisms (GMOs) in agriculture. While proponents claim the critical need for GE to address climate change and food security and assert its similarity to conventional breeding, critics argue that these technologies bring similar concerns to GMOs, such as supporting industrial agriculture and enhancing corporate control and ownership. But how do public groups make sense of these technologies? While incorporating public concerns is key to responsible and ethical innovation, minimal research explores how people make sense of emerging applications. We offer an exploratory Q study that investigates how one public group applies interpretive frames to understand applications of novel GE and related technologies. We find participants apply three different frames, invoking applications as (1) necessitating a system critical lens, (2) worthy of pragmatic of consideration, or (3) a deeply ambiguous prospect. These frames, we argue, articulate visions of particular sociotechnical futures, most of which are contrary or orthogonal to proponents’ assumptions. Instead, we find participants concerned less with the precision of techniques or the origin of genes used and more often with whether these applications reify dominant industrial practices and if viable alternatives exist.
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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 | 0.100 | 0.158 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.048 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".