Maturity Matters: How Ego Development Helps Chinese-Canadian Biculturals Flourish
Bibliographic record
Abstract
This study explores bicultural identity integration (BII) processes of adult Chinese-\nCanadians. Research has indicated that BII is generally associated with higher levels of\npsychological well-being in immigrants. During their bicultural integration, immigrants undergo\na significant process of personal development as they mature and become more capable in their\nnew cultural communities. Connections among processes of psychosocial maturity (Loevinger’s\nego development), well-being and bicultural identity provide the central focus for this\ninvestigation. All questionnaires in this investigation were presented in full bilingual format\nwith both English and Chinese translations for all questions. A moderation analysis examined\nways ego development may shape the relations between bicultural identity integration and\npsychological well-being. Using self-report instruments, data were collected online from a\nsample of 104 Chinese-Canadian bicultural adults. Results revealed that an overall model\nincorporating bicultural identity integration, ego development, and a moderation effect\nsignificantly predicted psychological well-being, explaining 26% of the variance of\npsychological well-being for our Chinese-Canadian bicultural sample. Examination of several\nfeatures of moderation patterns revealed a modest moderation trend involving the blendedness &\ncompartmentalization dimension of BII, p = .053, ΔR2 = .03, in explaining well-being. Although\nnot statistically significant, the trend offers substantive guidance for future research. The\nbilingual presentation of items provided an environment to simultaneously evoke both cultural\nframes for participants, as demonstrated in language use patterns and participant comments.\nThis pattern of results suggests that future research is warranted to further explore processes of\nbicultural integration development of Chinese-Canadian biculturals.
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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 | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".