Time-varying associations of racial discrimination and adjustment among Chinese-heritage adolescents in the United States and Canada
Bibliographic record
Abstract
The aim of our study was twofold: to examine (a) whether the link between racial discrimination and adjustment showed age-related changes across early to late adolescence for Chinese-heritage youth and (b) whether the age-related associations of the discrimination-adjustment link differed by gender, nativity, and geographical region. We pooled two independently collected longitudinal data sets in the United States and Canada (N = 498, ages 12-19 at Wave 1) and used time-varying effect modeling to show that discrimination is consistently associated with poorer adjustment across all ages. These associations were stronger at certain ages, but for males and females, first- and second-generation adolescents, and US and Canadian adolescents they differed. There were stronger relations between discrimination and adjustment in early adolescence for males compared to females, in middle adolescence for first-generation compared to second-generation adolescents, and in early adolescence for US adolescents compared to Canadian adolescents. In general, negative implications for adjustment associated with discrimination diminished across the span of adolescence for females, second-generation, and US and Canadian adolescents, but not for males or first-generation adolescents. The results show that the discrimination-adjustment link must be considered with regard to age, gender, nativity, and region, and that attention to discrimination in early adolescence may be especially important.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".