Racial differences in adolescent distress: Differential effects of the family and community for blacks and whites
Bibliographic record
Abstract
Abstract Using a sample of 15,885 adolescents derived from the National Longitudinal Study of Adolescent Health, this study examined (1) unique additive influences of race, family, and community and (2) various multiplicative influences among race, family, and community factors on adolescent distress. Community characteristics such as community poverty and ethnic composition were included in the analysis. Community poverty, family poverty, single parenthood, family size, and race/ethnicity all uniquely contributed to adolescent distress. There were significant black–white differences in additive and multiplicative influences of these predictors. The detrimental influence of family poverty was stronger for whites than for blacks. Among blacks, the detrimental influence of community poverty is greater for poor families than for nonpoor families. In contrast, among whites, the detrimental influence of community adversity is greater for nonpoor families than for poor families. Although ethnic composition had no significant impact on adolescent distress for the total sample, it showed a beneficial effect for black adolescents, after controlling for the poverty levels of the communities. Seemingly, community poverty and ethnic composition influence adolescent distress differently through different mechanisms. Understanding these complex processes raise some practical questions about programs aimed at minorities. For example, do black children fare better if their family overcomes persistent poverty and moves out of adverse communities? © 2005 Wiley Periodicals, Inc. J Comm Psychol 33: 261–282, 2005.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".