Examining the multidimensional nature of acculturation in a multi-ethnic community sample of first-generation immigrants
Bibliographic record
Abstract
While acculturation is among the most popular concepts in cultural psychiatry and psychology, the conceptualization and measurement of this construct continue to be issues of significant debate. Recent literature supports the development of multidimensional models of acculturation, in contrast to traditional linear or unidimensional models. The current study examined a multidimensional model of acculturation in a multi-ethnic community sample of first-generation immigrants in Montreal. Two cultural orientations - Canadian and Self-Defined Ethnicity - were assessed independently among Caribbean (n=109), Vietnamese (n=97), and Filipino (n=109) participants. These two cultural orientations were examined across three dimensions of acculturation: ethnic loyalty, ethnic behaviour, and situational ethnic identity. Correlational and factor analysis were used to assess the distinctiveness of the three dimensions, and the relationship between the two cultural orientations. For ethnic behaviour and situational ethnic identity, the two cultural orientations were not related to one another. Among the Vietnamese and Filipino groups, loyalty to one's self-defined ethnic group was positively related to loyalty towards Canadians. Factor analysis revealed two independent components, corresponding to the two cultural orientations. Overall, results support both the need to assess cultural orientations independently, and the multidimensional nature of 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".