Expert Views on Communicating Genetic Technology Used in Agriculture
Bibliographic record
Abstract
Abstract The use of genetic technology in agriculture is viewed by some as the next frontier of farming but others may view it as a threat. The aim of the current study was to describe the views of experts working in agricultural genetics regarding how best to communicate genetic technology with a broader audience (e.g., clientele, the public). We recruited 10 experts working in roles that involve communication about genetic technology in agriculture. Using semi-structured interviews, we asked participants to describe how they discuss this technology, who they discuss it with, and their thoughts on the involvement of various stakeholders in these discussions. Interview transcripts were subjected to thematic analysis and participant responses were organized into three themes: 1) Communicating and framing genetic technology, including discussing risks, benefits, and applications, distinguishing technology from other similar technologies, and engaging in value-based discussions; 2) Challenges of public communication, including misinformation and opposing opinions, conflation with older technologies, and balancing information provision; and 3) Stakeholder involvement in discussions, which included views on how different groups (e.g., activists, farmers, and scientists) should be included in discussions, and who is best suited to discuss genetic technology with the public. We conclude that leaders in agricultural genetics engage in a variety of approaches to communicate genetic technology, using different frames that they feel are likely to appeal to their audience, and differ in their opinions of who should be involved in these discussions.
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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 | 0.054 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".