The integration hypothesis: A critical evaluation informed by multilevel meta-analyses of three multinational datasets
Bibliographic record
Abstract
The integration hypothesis is the proposal that individuals who engage in both their heritage culture and in the larger society (by using the integration strategy) have better psychological adaptation than those using other strategies (by engaging with only one or neither cultural framework). This hypothesis has received substantial support over the years, but it has been recently contested by Bierwiaczonek and Kunst (2021). In their paper, which contributed new meta-analytic evidence, including some based on longitudinal studies, they argue that there is limited support for the role of acculturation strategies in adaptation and that contextual factors are more important than acculturation strategies for adaptation. Our paper shows that their correlational meta-analysis underestimates the integration-adaptation effect size, and that findings from the longitudinal meta-analysis do not appropriately support their claim. We present empirical findings in support of the integration hypothesis, particularly for positive indicators of adaptation (e.g., life satisfaction , self-esteem), and examine additional moderators using multilevel analyses of three multinational datasets: Nguyen and Benet-Martínez’s (2013) meta-analytic data, the MIRIPS dataset (Berry et al., 2022), and the ICSEY dataset (Berry et al., 2006). As a result, our study represents the largest test of the integration hypothesis to date. We complement these findings with theoretical work, relevant evidence from qualitative studies, and other meta-analyses and narrative reviews.
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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 | 0.356 | 0.641 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.016 | 0.033 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.014 | 0.010 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".