Climate solution or corporate co-optation? US and Canadian publics’ views on agricultural gene editing
Bibliographic record
Abstract
The dexterity and affordability of gene-editing technologies promise wide-ranging applications in agriculture. Aiming to take advantage of this, proponents emphasize benefits such as the climate-mitigating promises of gene editing. Critics, on the other hand, argue that gene editing will perpetuate industrialized forms of agriculture and its concomitant environmental and social problems. Across a representative sample of US and Canadian residents (n = 1478), we investigate public views and perceptions of agricultural gene editing. We advance existing survey-based studies, which tend to focus on whether knowledge, familiarity, trust, or perceptions of naturalness predict views on gene editing. Instead, we examine whether broader societal concerns about industrialized food systems-a key claim about genetic engineering launched by critics-predicts comfort with gene editing. We also explore the predictive power of views of climate change as an urgent problem, following proponent arguments. Survey results explore gene editing views in reference to specific cases (e.g., drought-tolerant wheat) and specific alternatives (e.g., versus pesticide use). We find that people critical of industrialized food systems were most likely to express overall absolute opposition to the technology, whereas those concerned with the imminence of climate change were more likely to support climate-relevant gene editing. Our findings suggest the need for further research into the conditions upon which public groups find gene editing compelling or not-namely, if applications enhance or counter industrial food systems, or offer particular climate adaptive benefits. Furthermore, we argue that attention to broader societal priorities in surveys of perceptions may help address calls for responsible research and innovation as concerns gene editing.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".