Factors Contributing to Personal Commitment in Chinese Interethnic Couples
Bibliographic record
Abstract
Interethnic relationships are increasingly common in many societies, yet interethnic couples have a higher divorce rate compared to intraethnic couples. Given these facts and the dearth of research, this study aimed to identify factors that contribute to couples’ commitment in interethnic relationships. This study investigated the personal commitment of Chinese interethnic couples in the United Kingdom and the United States. Specifically, whether love, dyadic adjustment and “couple cultural identity” (i.e. acculturation to the partner and couple’s similarity on individualism/collectivism) would predict personal commitment and whether each variable would account for unique variance in personal commitment of the participants. Thirty-seven Chinese/non-Chinese heterosexual couples participated in the study and significant relationships between love and personal commitment, dyadic adjustment and personal commitment were found. Also, couple cultural identity was important for women’s personal commitment. Multiple regression and structural equation modelling showed that partners in interethnic relationships defined personal commitment in different ways with men emphasizing love and dyadic adjustment, and women emphasizing love and acculturation to their partner. The discovery of the importance of couple cultural identity in contributing to personal commitment, besides love and dyadic adjustment, helps researchers to gain a greater understanding of such relationships and to extend the research on interethnic relationships.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".