Can Counter-stereotypic Information Reduce Prejudice Effects on Immigration?
Bibliographic record
Abstract
We use two experiments to assess the impact of information that counters negative stereotypes about (1) individual immigrants and (2) groups of immigrants who would be eligible for a large-scale increase in admissions on immigration policy preferences. The experiments are conducted in the US, Canada (Fr/Eng options available), Sweden, Netherlands, France, Italy, Germany, Spain, and the UK. In each experiment, we randomly vary immigrants' national origin and, independently, information about the individual or group's host country language proficiency and employment or this latter info plus information concerning socio-cultural integration and civic awareness as well as legal status. The dependent variables for the individual immigrant experiment are measures of support for allowing the immigrant to stay in the country, making immigrants like that eligible for participating in politics and receiving govt benefits, and a measure of shared social identification with the immigrant. The dependent variable for the group-level experiment is support for the policy to admit 50k immigrants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.013 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.019 | 0.008 |
| Open science | 0.014 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.327 |
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; both teacher heads agree on what is shown here.
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".