Majority members’ acculturation: How proximal-acculturation relates to expectations of immigrants and intergroup ideologies over time
Bibliographic record
Abstract
How do English majority members’ national culture maintenance and immigrant culture adoption (i.e., globalisation-based proximal-acculturation) predict their acculturation expectations (i.e., how they think immigrants should acculturate) and intergroup ideologies (i.e., how they think society should manage diversity)? Cross-sectional results ( N = 220) supported hypothesised relationships using a variable- and person-centred approach: welcoming expectations/ideologies related positively to immigrant culture adoption (or an integration/assimilation strategy) and negatively to national culture maintenance (or a separation strategy), whilst the reverse was true for unwelcoming expectations/ideologies. Notably, colourblindness showed only weak correlations with/differences across acculturation orientations/strategies. In longitudinal analyses, adopting immigrants’ cultures increased the intergroup ideologies polyculturalism and multiculturalism whilst reducing support for assimilation over time, whereas national culture maintenance had the opposite effect. Meanwhile, the expectation integration-transformation was especially related to higher odds of following an integration rather than separation strategy over time. Overall, results advance the psychological study of multiculturalism, providing first longitudinal insights on majority members’ acculturation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".