Young children of Black immigrants in America : changing flows, changing faces
Bibliographic record
Abstract
This book examines the well-being and development of children in black immigrant families (most with parents from Africa and the Caribbean). There are 1.3 million such children in the United States. While children in these families account for 11 percent of all black children in America and represent a rapidly growing segment of the U.S. population, they remain largely ignored by researchers. To address this important gap in knowledge, the Migration Policy Institute's (MPI) National Center on Immigrant Integration Policy embarked on a project to study these children from birth to age ten. Chapters include analysis of the changing immigration flow to the United States; the role of family and school relationships in the well-being of African immigrant children; exploration of the effects of ethnicity and foreign-born status on infant health; and parenting behaviour, health, and cognitive development among children in black immigrant families. Contributors include Randy Capps (MPI), Dylan Conger (George Washington University), Cati Coe (Rutgers University-Camden), Danielle A. Crosby (University of North Carolina-Greensboro), Angela Valdovinos D'Angelo (University of Chicago), Elizabeth Debraggio (New York University), Fabienne Doucet (Steinhardt School of Culture, Education, and Human Development), Sarah Dryden-Peterson (University of Toronto), Angelica S. Dunbar (University of North Carolina-Greensboro), Tiffany L. Green (Virginia Commonwealth University), Megan Hatch (George Washington University), Donald J. Hernandez (Hunter College and City University of New York), Margot Jackson (Brown University), Kristen McCabe (MPI), Lauren Rich (University of Chicago), Amy Ellen Schwartz (New York University), Julie Spielberger (University of Chicago), and Kevin J. A. Thomas (Pennsylvania State University).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".