Of mice and machines: The asymmetric dehumanization of immigrants as a function of perceived economic contribution
Bibliographic record
Abstract
Anti-immigrant research has often focused on the animalistic dehumanization of immigrants and refugees, with little focus on the antecedents of the mechanistic dehumanization of immigrants. In the present study, we investigated the asymmetric dehumanization of immigrants as a function of potential economic contribution, and economic thinking about immigration. We recruited participants (N = 500) from Prolific Academic, measuring beliefs about immigration and assessing the extent to which they subtly or blatantly dehumanized different immigrant groups. While we found no evidence for subtle dehumanization, we found that immigrants ostensibly selected for their educational ability and potential for economic contribution (i.e., economic immigrants, temporary foreign workers, international students) were more blatantly mechanistically (versus animalistically) dehumanized and were more blatantly mechanistically dehumanized compared to non-economic migrants (family class migrants and refugees). Further, economic thinking about immigration was associated with greater blatant mechanistic, but not animalistic dehumanization of immigrants, while beliefs about immigrant’s cultural contribution was associated with decreased blatant dehumanization overall.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".