Bridging Borders: Navigating the Tapestry of Multiculturalism and Migration
Bibliographic record
Abstract
This paper is based on a keynote delivered at the International Association for Cross-Cultural Psychology (IACCP) conference in Bali, Indonesia, in August 2024. It explores key themes related to social cohesion and inclusive intergroup relations in an era of rising global migration, exclusionary political rhetoric, and the surge of right-wing populism. First, the Multidimensional Individual Difference Acculturation (MIDA) model is reviewed. This framework examines how psychological resilience, social support, identity, cultural competence, and acculturation strategies influence adaptation outcomes across diverse migration contexts. Second, the interaction between individual and societal factors in shaping immigrant adaptation is analysed through empirical studies testing the MIDA model across various migration settings. Third, a comparative review of migrant integration policies, multiculturalism, interculturalism, and assimilation, is provided. Fourth, findings from global surveys are presented, highlighting the most and least accepting countries and their sociopolitical characteristics. In conclusion, the paper underscores the need for inclusive policies that go beyond cultural recognition to ensure meaningful structural inclusion.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.030 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".