Through the cultural looking glass: Diversity ideologies and cultural sharing in intercultural romantic relationships
Bibliographic record
Abstract
Intercultural romantic relationships are increasingly common. While past work has focused on how satisfied intercultural couples are compared to monocultural couples, we focus on factors within intercultural relationships that predict partners’ relationship quality. We propose that diversity ideologies—people’s beliefs about cultural diversity—are one set of factors that influence communication about cultural differences and relationship quality. Across two cross-sectional studies of individuals and one longitudinal study of couples in intercultural relationships ( N total = 838), we found that people who endorsed colorblindness—ignored cultural differences—expressed their own culture more but accepted their partner’s culture less in the relationship, in turn experiencing mixed relational outcomes. However, participants who endorsed multiculturalism—acknowledged cultural differences and aimed to preserve cultures as distinct—or polyculturalism—recognized cultural differences and viewed cultures as interconnected—expressed their own culture and accepted their partner’s culture more and in turn experienced higher relationship quality. Our studies provide the first empirical examination of how diversity ideologies shape the way intercultural couples communicate about their cultural differences and subsequently impact their relationship quality.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".