Multiracial Multilinguals: How Language Influences Multiracials’ Interpersonal Relationships
Bibliographic record
Abstract
Lovers in interracial couples are more likely to have different heritage languages and first languages than same-race couples, and Multiracials are the offspring of interracial couples. As such, Multiracials’ linguistic socialization likely differs from that of monoracials. Our study examined the influence of ethnoracial socialization on monoracials’ and Multiracials’ linguistic knowledge and the influence of language and ethnoracial identity on individuals’ interpersonal relationships. We hypothesized that monoracials would be more likely to know their heritage language than Multiracials, that linguistic knowledge would differ between interminority Multiracial groups and half-White Multiracial groups, and that linguistic knowledge would be associated with the formation of both friendships and romantic relationships. Our sample included nearly a thousand students from a university in California. We found that monoracial minorities were more likely to be multilingual than both interminority Multiracials and half-White Multiracials. East Asian participants who spoke an East Asian language had a higher proportion of East Asian friends. Among Multiracials, Wasians and Latinasians who spoke an East Asian language primarily dated someone East Asian, Latinasians who spoke a Latin American language primarily dated someone Hispanic, and monolinguals of both groups were the ones most likely to date someone White. These findings suggest that linguistic knowledge plays a significant role in a community's social dynamics, affecting monoracials' and (especially) Multiracials' interpersonal 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".