Self-consistency in Bicultural Persons: Dialectical Self-beliefs Mediate the Relation between Identity Integration and Self-consistency
Bibliographic record
Abstract
Prior research differentiates dialectical (e.g., East Asian) from non-dialectical cultures (e.g., North American and Latino) and attributes cultural differences in self-concept consistency to naïve dialecticism. In this research, we explored the effects of managing two cultural identities on consistency within the bicultural self-concept via the role of dialectical beliefs. Because the challenge of integrating more than one culture within the self is common to biculturals of various heritage backgrounds, the effects of bicultural identity integration should not depend on whether the heritage culture is dialectical or not. In four studies across diverse groups of bicultural Canadians, we showed that having an integrated bicultural identity was associated with being more consistent across roles (Studies 1-3) and making less ambiguous self-evaluations (Study 4). Furthermore, dialectical self-beliefs mediated the effect of bicultural identity integration on self-consistency (Studies 2-4). Finally, Latino biculturals reported being more consistent across roles than did East Asian biculturals (Study 2), revealing the ethnic heritage difference between the two groups. We conclude that both the content of heritage culture and the process of integrating cultural identities influence the extent of self-consistency among biculturals. Thus, consistency within the bicultural self-concept can be understood, in part, to be a unique psychological product of bicultural experience.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".