It ain’t easy eating greens: Evidence of bias toward vegetarians and vegans from both source and target
Bibliographic record
Abstract
Vegetarianism and veganism are increasingly prevalent in Western countries, yet anecdotal expressions of negativity toward vegetarians and vegans are common. We empirically tested whether bias exists toward vegetarians and vegans. In Study 1 omnivores evaluated vegetarians and vegans equivalently or more negatively than several common prejudice target groups (e.g., Blacks). Bias was heightened among those higher in right-wing ideologies, explained by heightened perceptions of vegetarian/vegan threat. Vegans (vs. vegetarians) and male (vs. female) vegetarians/vegans were evaluated more negatively overall. In Study 2 omnivores evaluated vegetarians and vegans more negatively than several nutritional outgroups (e.g., gluten intolerants) and evaluated vegan/vegetarians motivated by animal rights or environmental concerns (vs. health) especially negatively. In Study 3, vegetarians and especially vegans reported experiencing negativity stemming from their diets. Empirically documenting antivegetarian/vegan bias adds to a growing literature finding bias toward benign yet social norm-challenging others.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".