Exploring the roots of dehumanization: The role of animal—human similarity in promoting immigrant humanization
Bibliographic record
Abstract
Little is known about the origins of dehumanization or the mechanisms through which dehumanization impacts outgroup prejudice. We address these issues by measuring and manipulating animal—human similarity perceptions in a human intergroup context. As predicted, beliefs that animals and humans are relatively similar were associated with greater immigrant humanization, which in turn predicted more favorable immigrant attitudes (Study 1). Those higher in Social Dominance Orientation (SDO) or lower in Universal Orientation particularly rejected animal—human similarity beliefs, partially explaining their increased tendency to dehumanize and reject immigrants. In Study 2, perceptions of animal—human similarity were experimentally induced through editorials highlighting similarities between humans and other animals or emphasizing the human—animal divide. Emphasizing animals as similar to humans (versus humans as similar to animals, or the human—animal divide) resulted in greater immigrant humanization (even among highly prejudiced people). This humanization process facilitated more re-categorization (i.e., inclusive intergroup representations between immigrants and Canadians) and increased immigrant empathy, both of which predicted less prejudicial attitudes toward immigrants. Implications for research, theory, and interventions for dehumanization and prejudice are considered.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".