Party Identification, Contact, Contexts, and Public Attitudes toward Illegal Immigration
Bibliographic record
Abstract
Illegal immigration is a contentious issue on the American policy agenda. To understand the sources of public attitudes toward immigration, social scientists have focused attention on political factors such as party identification; they have also drawn on theories of intergroup contact to argue that contact with immigrants shapes immigration attitudes. Absent direct measures, contextual measures such as respondents’ ethnic milieu or proximity to salient geographic features (such as borders) have been used as proxies of contact. Such a research strategy still leaves the question unanswered – is it contact or context that really matters? Further, which context, and for whom ? This article evaluates the effects of party identification, personal contact with undocumented immigrants, and contextual measures (county Hispanic population and proximity to the US–Mexico border) on American attitudes toward illegal immigration. It finds that contextual factors moderate the effects of political party identification on attitudes toward illegal immigration; personal contact has no effect. These findings challenge the assumption that contextual measures act as proxies for interpersonal contact.
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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 | 0.002 | 0.005 |
| 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 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".