Perceived Discrimination Experiences Among Multiracial Children in the ABCD Study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES Racism and discrimination impact children’s health; there is little information about Multiracial children. The US Multiracial population grew from 2.9% in 2010 to 10.2% in 2020. This study investigates associations between racial and ethnic identification and perceived discrimination among Multiracial children in the multicenter Adolescent Brain Cognitive Development Study (ABCD Study). METHODS Children were recruited at 9 to 10 years old in 2016 to 2018. Caregivers reported racial and ethnic identification; children self-reported experiences of discrimination. Generalized linear models with logit link function were used to assess associations between identity and discrimination experiences. Adjusted odds ratios (ORs) and adjusted P values were reported. RESULTS One thousand one hundred twenty-four children (10.8%) identified as Multiracial. The largest Multiracial groups were white-Black (3.4%), white-Asian (3.0%), and white–American Indian/Alaska Native (1.6%). The largest monoracial groups were white (65.4%) and Black (16.3%). White-Black participants had higher odds than their white monoracial counterparts of perceiving discrimination by other adults outside of school (OR, 2.16 [95% CI, 1.24–3.77]; P = .014) and other students (OR, 1.65 [95% CI, 1.17–2.32]; P = .012); feeling that others behaved unfairly or in a negative way toward their ethnic group (OR, 1.78 [95% CI, 1.16–2.72]; P = .014); feeling like other Americans had something against them (OR, 2.50 [95% CI, 1.48–4.23]; P < .001); and feeling discriminated against over the past 12 months due to race, ethnicity, or color (OR, 2.40 [95% CI, 1.51–3.82]; P < .001). CONCLUSION Multiracial children perceive discrimination at an early age and have different experiences based on race and ethnicity, and some groups have higher odds of certain types of discrimination than their component identity groups. These experiences impact health outcomes through complex pathways.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".