Critical reflections on "humane" meat and plant-based meat "alternatives"
Bibliographic record
Abstract
Canadians are among the top meat consumers in the world. Greenhouse gas emissions, biodiversity loss, animal stress and suffering, worker health and safety, and cardiovascular disease are among the multitude of issues tied to high rates of meat consumption. In response to rising concern and debate over the impacts of meat consumption, two sectors of the food industry have grown considerably in recent years: "humane" meat and plant-based meat "alternatives." The former attempts to ameliorate harms via more ethical farming practices, and the latter utilizes technological innovations to replace animal-based meat. In this article, we outline a dilemma wherein pathways to more sustainable and ethical food systems may require both an acceptance of these solutions and a push beyond them towards more complex, systemic changes. We conclude with a brief discussion of critical food guidance, and the potential roles of law, regulation, and policy in driving incremental but important changes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.055 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.024 | 0.025 |
| 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".