The Elephant in the Room: The Often Neglected Relevance of Speciesism in Bias Towards Ethnic Minorities and Immigrants
Bibliographic record
Abstract
The area of intergroup bias and, specifically attitudes towards ethnic minorities and/or immigrants, has received a great amount of investigation by (cross-cultural) psychologists, spanning many theories and perspectives (Hewstone et al., 2002). However, one perspective rarely taken in mainstream psychology is one that acknowledges the inter-linkage of bias towards ethnic minorities and/or immigrants and that towards non-human animals (NHAs), despite relatively substantial literature outside of psychology emphasizing it (Singer, 2002). In the present paper, we draw from relevant literature outside and inside of psychology that speaks to the connectivity between attitudes towards marginalized human outgroups and NHAs, focusing on the mechanism of dehumanization in intergroup bias. We also shed light on more recent psychological research, specifically the Interspecies Model of Prejudice (IMP; Costello & Hodson, 2010; Costello & Hodson, 2014a; 2014b) as an example on how psychological research could incorporate speciesism into the discussion of intergroup bias. It is hoped that highlighting the existing rare, yet valuable, research endeavours within psychology inspires further engagement from psychologists interested in cross-cultural, intersectional, and diversity research in order to help better the lives of both marginalized human outgroups and NHAs.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.030 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".