Blatant Dehumanization is Not Influenced by Dual Identity Labels: Evidence from the Canadian Context
Bibliographic record
Abstract
Blatant dehumanization has been shown to be prevalent in modern society. However, little work has explored the possible ways in which blatant dehumanization may be attenuated. The current study addresses this gap in the literature by exploring if activating a dual identity attenuates (or even erases) blatant dehumanization. To investigate these issues, Canadian participants completed the “Ascent of Man” scale, rating various groups in terms of their perceived evolutionary qualities. Half of our participants saw labels with the qualifier “-Canadians” attached, while the other half saw no such qualifier. Results showed that, regardless of whether the “-Canadians” label was provided, participants rated Filipinos, Christians, Arabs, Muslims, and Indigenous groups as significantly lower than Whites on the evolution scale. As such, provision of the additional group label “-Canadians” did not influence the manifestation of blatant dehumanization. We also found that ratings on the evolution scale significantly correlated with both Social Dominance Orientation and Empathic Concern levels, such that stronger adherence to current power structures and social hierarchies showed stronger blatant dehumanization, while those with a high pre-disposition for altruistic behaviours and emotions showed weaker blatant dehumanization. We discuss our results in the light of other research on blatant dehumanization and intergroup processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".