Race, Class, and Ethnicity in Young Adulthood
Bibliographic record
Abstract
Abstract This chapter examines how social class, race, and ethnicity shape the transition to adulthood, drawing heavily on life course theory, developmental contextualism, and the concepts of risk, resilience, and social capital as analytic anchors. Four domains of functioning are discussed—conceptions of adulthood, mental health, paid employment, and educational attainment. Emphasis is given to studies that illuminate processes that link social location to the development of youth (mediational processes) and studies that examine sources of variation in developmental pathways among economically disadvantaged and ethnic minority youth, with special attention to the positive end of the risk dimension (i.e., promotive factors). We also highlight protective factors, that is, factors and processes that mitigate the negative effects of economic advantage and ethnic minority status on the functioning of youth during the transition to adulthood. Each major section of the chapter concludes with a summary and discussion of questions and issues that merit study in future research. The chapter emphasizes research based on U.S. samples, but incorporates research findings from youth in Canada and Western European countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".