Comparison of personality traits of two anti‐oppression groups: Vegans and anarchists
Bibliographic record
Abstract
Abstract Veganism and anarchism are burgeoning worldwide, yet very few studies have examined the psychological characteristics of people belonging to these two anti‐oppression groups. The present study investigated whether vegans and anarchists, on the one hand, and activists and non‐activists belonging to these two groups, on the other hand, exhibit distinct personality profiles. To this end, a sample of 180 adults who self‐identify as vegans or anarchists completed an online socio‐demographic questionnaire, the HEXACO Personality Inventory, and the Dark Triad Dirty Dozen. A discriminant function analysis showed that anarchists are more likely than vegans to self‐identify as belonging to a gender other than female or male, or to identify with no gender at all. Further, the proportion of men was larger in the anarchist group than in the vegan group. In terms of personality traits, vegans scored higher on the Conscientiousness, Emotionality, and Honesty–Humility dimensions than anarchists did. Anarchists scored higher than vegans on Openness to Experience and Psychopathy. Activists and non‐activists were not distinguished based on gender or personality traits. While the dynamics of power and oppression toward humans and toward animals share common factors, the present results suggest that veganism and anarchism attract anti‐oppression advocates with distinct personality profiles.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".