The Stakes in Steak: Examining Barriers to and Opportunities for Alternatives to Animal Products in Canada
Bibliographic record
Abstract
This Article considers some of the different food innovations being presented as potential solutions to the myriad problems associated with conventional models of industrial agriculture. Specifically, in vitro meat (IVM) and plant-based alternatives to animal products—and their corresponding regulatory and market structures— are compared and contrasted. Examining the idiosyncrasies around Canada’s approach to regulating these products reveals that the respective degrees of scrutiny may not be commensurate with the respective degrees of risk, due in part to the influence of powerful industry actors who wish to maintain the status quo. Given the significance and scope of the problems implicated by the industrial food production system, favouring special economic interests comes at the detriment of a much wider group of stakeholders. As such, the governance of new food innovations requires a more critical and thoughtful approach if it is to better reflect shared aspirations for a more just and sustainable food system for all. Cet article porte sur différentes innovations alimentaires présentées comme des solutions potentielles à la myriade de problèmes associés aux modèles conventionnels d'agriculture industrielle. Plus précisément, la viande in vitro (VIV) et les alternatives végétales aux produits d'origine animale—et leurs structures réglementaires et commerciales correspondantes—sont comparées et mises en contraste. L'examen des particularités de l'approche du Canada en matière de réglementation de ces produits révèle que la rigueur des analyses respectives n'est peut-être pas proportionnelle aux degrés respectifs de risque, en partie à cause de l'influence d'acteurs industriels puissants qui souhaitent maintenir le statu quo. Compte tenu de l'importance et de l'ampleur des problèmes posés par le système de production alimentaire industrielle, favoriser des intérêts économiques particuliers se fait au détriment d'un groupe beaucoup plus large d'acteurs. En tant que telle, la gouvernance des nouvelles innovations alimentaires exige une approche plus critique et réfléchie si l'on veut qu'elle reflète mieux les aspirations communes à un système alimentaire plus juste et durable pour tous.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".