Vegetarian, vegan, activist, radical: Using latent profile analysis to examine different forms of support for animal welfare
Bibliographic record
Abstract
There are many different ways that people can express their support for the animals that exist in factory farms. This study draws on insights from the social identity approach, and adopts novel methods (latent profile analysis [LPA]) to examine the qualitatively different subgroups or profiles that comprise broader community positions on this issue. North American participants ( N = 578) completed measures of the frequency with which they engaged in 18 different animal welfare actions. LPA identified 3 meaningful profiles: ambivalent omnivores ( n = 410; people who occasionally limited their consumption of meat/animal products), a lifestyle activist group ( n = 134; limited their consumption of animal/meat products and engaged in political actions), and a vegetarian radical group ( n = 34; strictly limited their consumption of animal/meat products and engaged in both political and radical actions). Membership of the 3 populations was predicted by different balances of social identities (supporter of animal welfare, vegan/vegetarian, solidarity with animals), and markers of politicization and/or radicalization. Results reveal the utility of adopting person-centred methods to study political engagement and extremism generally, and highlight heterogeneity in the ways that people respond to the harms perpetrated against animals.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".