An integrated psychology of (animalistic) dehumanization requires a focus on human-animal relations
Bibliographic record
Abstract
Relative to the study of prejudice and stereotyping, the systematic study of how we animalize outgroup members is a newcomer to the study of intergroup relations. With remarkable gains made in the last two decades, the field is now represented by distinct methods and approaches emphasized across camps, with recent calls for conceptual integration (see this Special Issue). Our central contention is that the existing literature focuses too much on humans (and the psychological stripping away of humanness from targets) with insufficient attention to animals, particularly regarding how we think about and treat animals (i.e., human-animal relations). How and why we animalize other people is systematically linked to how we overvalue humans relative to other animals; dehumanization of other people carries its sting and clout because animals are disregarded or exploited as entities deserving less protection and fewer rights relative to humans. We argue that the dehumanization field would benefit from this perspective, including the introduction of novel interventions, but also that the spillover benefits would help us to better understand human nature and our future challenges.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.054 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".