Bibliographic record
Abstract
This paper analyzes immigrants' views about immigration, contributing to the behavioral literature on the subject. In particular, it explores the role of statistical discrimination as a cause of possible opposition to immigration in the absence of stringent immigration policies and the large amount of undocumented immigration. We test this hypothesis using US data from the seventh wave of the World Value Survey, finding that successful immigrants in the United States (i.e., those who are in the top quintile of the socioeconomic classification), who may benefit the most from being perceived as unrelated to unskilled undocumented immigrants, have negative views about immigration, especially with respect to its contribution to unemployment, crime, and the risk of a terrorist attack. This effect does not arise in the case of countries that apply stricter controls than the United States on immigration, like Australia, Canada, and New Zealand, or do not attract as large a number of undocumented immigrants. We interpret these results as evidence that immigrants' attitudes toward other immigrants respond to the lack of a selective immigration policy: namely, if successful immigrants run the risk of being perceived as related to undocumented or uncontrolled immigration, they respond by embracing an immigrants’ anti-immigration view.
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 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 | 0.002 | 0.011 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".