Implicit bias against non-Americans in the United States: Cognitive underpinnings and ecological correlates
Bibliographic record
Abstract
Of the 330 million residents of the United States, over 40 million were born abroad. Such individuals are routinely referred to using labels such as “alien,” “foreigner,” and “noncitizen.” In this multimethod project relying on data from 5,437 U.S. citizens in experimental studies and 125,126 U.S. citizens in archival studies, we examine implicit (automatic) evaluations of non-Americans in the United States, their effects on impression formation, and their ecological correlates in the form of real-life outcomes. In Studies 1A–1C, the labels “alien,” “foreigner,” and “noncitizen” were found to be highly and similarly implicitly negative. In Studies 2A–2D, applying these labels to specific individuals created immediate implicit negativity toward them, irrespective of their gender or race. Finally, pro-American/anti-foreigner implicit evaluations predicted anti-immigrant policy positions at the level of individuals (Study 3A), and a conceptually and statistically related implicit White–American/Asian–foreign implicit stereotype predicted anti-immigrant voting patterns in 18 relevant ballot initiatives at the level of U.S. counties (Study 3B). Across studies, implicit anti-foreigner bias generalized across participant demographics but was somewhat stronger among men and political conservatives. Together, this work highlights the cognitive underpinnings and real-world correlates of robust and pervasive anti-foreigner biases in the United States.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".