“Reasonable suspicion” about tough immigration legislation: Enforcing laws or ethnocentric exclusion?
Bibliographic record
Abstract
We examined whether support for tough immigration legislation reflects identity-neutral enforcement of law or identity-relevant defense of privilege. Participants read a fabricated news story in which law-enforcement personnel detained a person due to "reasonable suspicion" that he was an undocumented immigrant. We manipulated descriptions of the detainee so that he was either (a) an undocumented immigrant (both studies), (b) a documented immigrant (Study 1), or (c) a U.S. citizen (Study 2) of either Mexican or Canadian origin. Participants in both studies endorsed tougher punishment of an undocumented detainee and rated tough treatment as more fair when the detainee was of Mexican than Canadian origin (regardless of documentation status). Across both studies, the patterns of ethnocentric exclusion-harsher treatment toward Mexican immigrants than Canadian immigrants-were particularly pronounced among participants who defined American identity in terms of assimilation to Anglocentric cultural values (e.g., being able to speak English). Overall, results suggest that people may support tough measures to restrict immigration to defend against symbolic threats-especially threats that cultural "others" pose to Anglocentric understandings of American identity.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".