Cutting in line ahead of us: the role of group relative deprivation in shaping gatekeeping attitudes across different immigrant integration contexts in Europe
Bibliographic record
Abstract
Although research shows that anti-immigrant sentiments are generally lower in liberal integration policy contexts, popular backlash against immigrants become salient in many pro-immigration and inclusive integration policy contexts in Europe today. Developing a contextualized mediation model, this research suggests that feelings of deprivation vis-à-vis immigrants influence attitudes toward selective immigrant admission in Europe. From a cross-country analysis of the 2014–2015 European Social Survey through multigroup structural equation modeling, our findings reveal that sentiments of group relative deprivation translate into stronger gatekeeping attitudes throughout Europe by developing threat perceptions from immigration. Relative deprivation-driven threat perceptions influence gatekeeping attitudes more potently in countries where integration policies grant immigrants more comprehensive and equal rights, while they remain relatively dormant in countries with exclusionary integration policies. These findings contribute to our understanding of how and to what extent relative deprivation sentiments vis-à-vis immigrants shape gatekeeping attitudes while shedding light on the unintended impacts of liberal integration policies on public opinion regarding immigration.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".