Engaging publics on gene editing of farm animals: Evidence from the online gray literature
Bibliographic record
Abstract
Researchers are using diverse strategies to engage publics on what they think about emerging biotechnologies in agriculture, like gene editing applications for farm animals. As anyone can now access gray literature when seeking information on the Internet, we reviewed online gray literature as a dataset to understand public engagement strategies. We found 34 documents through our online search and expert consultation, and categorized documents based on the engagement strategy used: consulting, informing, or marketing. We then discussed how publics were involved in these strategies, analyzing the approaches across the theoretical frameworks of Social License to Operate, Responsible Research and Innovation, and Deliberative Democracy. Reviewing the gray literature offers a unique perspective into how organizations seek to influence public debates about gene editing. Individual sources within gray literature often presented a combination of engagement strategies based on the entity’s objectives. Our findings suggest that informing and marketing are the most common strategies because of the goal to influence policy and governance. We conclude that the gray literature can be used to document how organizations seek to engage various publics about gene editing applications, suggesting this largely underutilized dataset might offer a means of bridging policy and academic debates about the issue.
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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 | 0.058 | 0.218 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.025 | 0.025 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".