Sociotechnical imaginaries of gene editing in food and agriculture: A comparative content analysis of mass media in the United States, New Zealand, Japan, the Netherlands, and Canada
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
Sociotechnical imaginaries of gene editing in food and agriculture reflect and shape culturally particular understandings of what role technology should play in an ideal agrifood future. This study employs a comparative media content analysis to identify sociotechnical imaginaries of agricultural gene editing and the actors who perform them in five countries with contrasting regulatory and cultural contexts: Canada, Japan, New Zealand, the Netherlands, and the United States. We find that news media in these countries reinforce a predominantly positive portrayal of the technology's future, although variations in which imaginaries are most mobilized exist based on the regulatory status of gene editing and unique histories of civil society engagement around biotechnology in each country. We argue that by granting legitimacy to some narratives over others, the media supports gene editing as a desirable and necessary component of future agrifood systems, thereby limiting consideration of broader issues related to the technology's development and application.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Qualitative | low |
| gpt | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Qualitative | high |
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it