The Role of Early Immersive Culture Mixing in Cultural Identifications of Multiculturals
Bibliographic record
Abstract
Becoming multicultural through early immersive culture mixing (EICM)—i.e., growing up with a mix of cultures that coexist and interact to form an emergent hybrid culture within one’s home—is a rapidly rising phenomenon in many parts of the world. This phenomenon calls for new research that recognizes the possibility of identification with a hybrid culture as well as the distinct cultures from which the hybrid culture derives. This article extends previous research into psychological variation among multiculturals based on the process of EICM, by investigating how EICM influences hybrid cultural identification and distinct cultural identification. In addition, we examine how EICM relates to the components of identity integration—blendedness and harmony. Across two studies of Chinese-Australian multiculturals, we found that whereas EICM was positively associated with multicultural participants’ identification with a hybrid culture and Australian culture, it was not related to their identification with Chinese culture. Findings also indicated that EICM positively predicted identity blendedness, but EICM did not show a clear link with identity harmony. We discuss the implications of our research for advancing EICM theory and helping to forge new research directions in cultural identification.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".