Public Inclusion and Responsiveness in Governance of Genetically Engineered Animals
Bibliographic record
Abstract
Abstract Genetically engineered (GE) animal-based foods have entered the Canadian market in recent years, yet a significant proportion of the public is reticent to consume them. Responsible innovation has been suggested as a paradigm for bolstering democratic processes and aligning societal values with technology research and development. In this chapter, we examine regulatory decision-making for the first GE animal approved for food consumption in Canada, the AquAdvantage Salmon (AAS), according to two principles of responsible innovation (RI)— inclusion and responsiveness . First, we look at the regulatory approval process for AAS to examine when there were opportunities for public and stakeholder participation in decision-making (inclusion ). Second, we report on our studies using textual analysis of one public participation window—a series of Parliamentary hearings associated with GE animal oversight in Canada in 2016. Here, we examine whether decision-makers incorporated the diverse stakeholder perspectives and concerns voiced at the hearings into their final reports ( responsiveness ). Finally, we identify barriers to putting inclusion and responsiveness into practice in risk governance of GEOs and discuss ways to overcome these barriers to facilitate responsible innovation practices in oversight systems for emerging technologies.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".