Concerns About Automation and Negative Sentiment Toward Immigration
Bibliographic record
Abstract
= 31,581), we examined how concerns about the rise of automation may be associated with attitudes toward immigrants. Studies 1a to 1g used archival data ranging from 1986 to 2017 across both the United States and Europe to demonstrate a robust association between concerns about automation and more negative attitudes toward immigrants. Studies 2a, 2b, 2c, and 3 employed both correlational and experimental methods to demonstrate that people's concerns about automation are linked to increased support for restrictive immigration policies. These studies show this association to be mediated by perceptions of both realistic and symbolic intergroup threat. Finally, Study 4 experimentally demonstrated that automation may lead to more discriminatory behavior toward immigrants in the context of layoffs. Together, these results suggest that concerns about automation correspond to perceptions of threat and competition with immigrants as well as consequent anti-immigration sentiment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".