Bibliographic record
Abstract
More than 20,000 white Americans go abroad each year to adopt children from other countries, the majority of whom are not white. At the same time, there are more African-American children available for adoption than there are African-American families seeking to adopt them. While Americans claim there are few healthy infants available for adoption in the United States, hundreds of African-American newborns each year are placed with white families in Canada and other countries. Tracing the history of transracial adoption in the United States, Professor Maldonado argues that one reason Americans go abroad to adopt is race. The racial hierarchy in the adoption market places white children at the top, African-American children at the bottom, and children of other races in between, thereby possibly rendering children from Asia or Latin America more desirable to adoptive parents than African-American children. Drawing on the rich literature on cognitive bias, Professor Maldonado debunks the myths about domestic and international adoptions and shows that racial preferences, even if unconscious, play a role in many Americans' decisions to adopt internationally. She proposes that the law discourage international adoptions based on racial preferences by requiring that Americans seeking to adopt internationally, while there are available children in the United States who meet their non raced based criteria, show non-race-based reasons for going abroad.
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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".