The role of natural scientists in navigating the social implications of cellular agriculture: insights from an interdisciplinary workshop
Bibliographic record
Abstract
The emerging field of cellular agriculture uses cell culture to create animal products, potentially mitigating climate and health risks associated with conventional animal agriculture. However, cellular agriculture products are poised to enter the food ecosystem without an understanding of the long-term consequences and social implications. While these discussions have begun among social scientists, dialogues are lacking among natural scientists and engineers, perpetuating a disconnect between those progressing new technology and those most directly impacted by it. To begin to bridge this gap, an interdisciplinary workshop was organized by the Food and Agriculture Institute at the University of the Fraser Valley in collaboration with the Arrell Food Institute, New Harvest, and Cellular Agriculture Canada. At his workshop, representatives from cellular agriculture companies, STEM research labs, dairy farms, animal rights organizations, and Indigenous communities convened to discuss the social implications of cellular agriculture. Specific topics of interest were food security, labor, and employment, power relations and governance, and animal ethics. In this commentary, the authors highlight critical learnings from the workshop as natural scientists, namely the relationship between food and identity, the variety of human-animal relationships, and implications for nutrition and health. We believe that for a just transition of our food systems, the development of cellular agriculture needs to include communities as collaborators from the outset. While this work is difficult in the current environment of market capitalism, it has the potential to improve the culture of research and development to benefit the broader society. To this end, we provide resources, examples, and invitations to natural scientists and researchers interested in engaging with this work. As we rapidly approach a food system that includes products created with cellular agriculture, we encourage readers to consider which individuals and populations need to be involved in this growth, and how they can work together to promote a sustainable future for all.
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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 | 0.036 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.033 | 0.031 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.014 | 0.020 |
| Insufficient payload (model declined to judge) | 0.004 | 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".