Support for Tough Immigration Policy: Identity Defense or Concern for Law and Order?
Bibliographic record
Abstract
Abstract Across two studies, U.S. participants read a fictional transcript of a law enforcement officer who observed a speeding infraction and made a discretionary traffic stop. The car carried occupants who displayed either high or low fit with Anglocentric constructions of U.S. identity and were of presumptive Mexican (Studies 1 and 2), Canadian (Study 1), or Irish (Study 2) origin. The officer decided over the course of the traffic stop that the occupants’ behavior aroused “reasonable suspicion” about documentation status, so he asked them to produce identification documents and detained them when they failed to do so. Participants indicated their suspicion about occupants’ documentation status and rated the appropriateness of law enforcement actions. Results indicate effects of origin across both studies for all outcomes: participants considered occupants of Mexican origin (vs. Canadian or Irish) as more suspicious, and rated law enforcement actions related to traffic and immigration violations as more appropriate when the interaction involved occupants of Mexican origin (vs. Canadian or Irish). Results indicate effects of fit across both studies for all outcomes: participants considered occupants who showed low‐fit (vs. high‐fit) as more suspicious, and rated law enforcement actions related to traffic and immigration violations as more appropriate when occupants showed low‐fit (vs. high‐fit). Discussion focuses on how participant support for punitive anti‐immigration measures is less about neutral enforcement of law than about racialized exclusion to defend an Anglocentric construction of U.S. 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 | 0.010 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".