The dynamics of attitudes toward immigrants: Cohort analyses for Western EU member states
Bibliographic record
Abstract
Public opinion climates on immigrants are subject to certain dynamics. This study examines two mechanisms for such dynamics in Western EU member states for the 2002–2018 period. First, the impact of cohort replacement and, second, the impact of periodic threat perceptions, namely, changing macroeconomic conditions and shifts in immigration rates. To date, empirical research on anti-immigrant sentiments rarely combines these two concepts simultaneously to disentangle the interplay of period and cohort effects and determine the factors for long- and short-term attitude changes in societies. Motivated by this gap in the literature, I conduct multiple linear regression analyses of pooled data from all waves of the European Social Survey to show that the process of cohort replacement has led to a substantially more positive opinion climate toward immigrants since the 2000s. However, results indicate that in the future, this positive development is likely to come to a halt since younger cohorts no longer hold significantly more immigrant-friendly attitudes than their immediate predecessors. Furthermore, we observe different period effects to impact cohorts’ attitudes. Fixed-effects panel analyses show that the effect of changing macroeconomic conditions on cohorts’ attitudes is low. Changes in immigration rates, however, lead to significantly more dismissive attitudes when immigrants originate from the Global South as opposed to when they enter from EU countries. These insights suggest that it is less economic or cultural threat perceptions, but ethnic prejudice that plays a key role for natives to oppose immigration. Overall, findings suggest that it is not either cohort or period effects driving large-scale attitude changes, but rather we observe an interplay of both.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".