Common Ideological Roots of Speciesism and Generalized Ethnic Prejudice: The Social Dominance Human–Animal Relations Model (SD–HARM)
Bibliographic record
Abstract
Recent research and theorizing suggest that desires for group–based dominance underpin biases towards both human outgroups and (non–human) animals. A systematic study of the common ideological roots of human–human and human–animal biases is, however, lacking. Three studies (in Belgium, UK, and USA) tested the Social Dominance Human–Animal Relations Model (SD–HARM) proposing that Social Dominance Orientation (SDO) is a key factor responsible for the significant positive association between ethnic outgroup attitudes and speciesist attitudes towards animals, even after accounting for other ideological variables (that possibly confound previous findings). Confirming our hypotheses, the results consistently demonstrated that SDO, more than right–wing authoritarianism (RWA), is a key factor connecting ethnic prejudice and speciesist attitudes. Furthermore, Studies 2 and 3 showed that both SDO and RWA are significantly related to perceived threat posed by vegetarianism (i.e. ideologies and diets minimizing harm to animals), but with SDO playing a focal role in explaining the positive association between threat perceptions and ethnic prejudice. Study 3 replicated this pattern, additionally including political conservatism in the model, itself a significant correlate of speciesism. Finally, a meta–analytic integration across studies provided robust support for SD–HARM and offers important insights into the psychological parallels between human intergroup and human–animal relations. Copyright © 2016 European Association of Personality Psychology
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".