Does “Precision” Matter? A Q Study of Public Interpretations of Gene Editing in Agriculture
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it