Understanding in same- versus cross-race close relationships predicts the well-being of people of color over time
Bibliographic record
Abstract
Opening up and feeling heard are central to close relationships; in fact, understanding and disclosure with close others are beneficial for individuals' well-being and quality of life. However, for people of color, understanding and disclosure may unfold differently depending on whether their close others share their racial/ethnic background. We examine this question with young Black, Latine, and Asian people in a cross-sectional national U.S. sample (N = 1285) and a weekly diary study (N = 101). In Study 1, young people of color felt more understood in same-race than in cross-race close relationships. Moreover, feeling understood in both types of relationships distinctly predicted depressive symptomatology one year and two years later. In Study 2, same-race understanding was uniquely associated with depressive affect and flourishing, but cross-race understanding was not. In both studies, same-race and cross-race disclosure did not differ or predict outcomes. Together, these findings suggest that young people of color disclose similarly in their close same-race and cross-race relationships but feeling understood is more directly associated with their psychological well-being.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".