Meat politics at the dinner table
Bibliographic record
Abstract
Few food groups are subject to the same depth and scope of critique as meat. Yet little is known about how the Canadian public feels about meat production and consumption. In other jurisdictions, meat has been a politically polarizing topic; thus, we focus our analysis on political differences (and similarities) in orientations toward meat. In this paper, we draw on survey data collected on a quota sample of Canadians (n=2328) in order to address the following questions: to what extent do Canadians across the political spectrum agree that meat is a problem? Where is there overlap, and where is there disagreement? We find that, despite small but statistically significant differences across political ideology in Canadians’ meat-related attitudes, preferences, and practices, there is widespread agreement that meat is delicious, that it poses risks to health, and that many livestock production practices violate animal welfare ethics. The majority of Canadians would prefer to source meat that is locally-produced and raised on a small farm. These patterns illustrate high levels of discomfort with large-scale animal agriculture. This study fills an important gap in Canadian food studies by interrogating public perceptions of meat and identifying areas of political convergence and divergence on meat-related attitudes, preferences, and practices.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.049 | 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".