What’s Race Got to Do with It? Exploring the In-Race Adoption of Asian children
Bibliographic record
Abstract
This study sought to explore adoption in Asian American families. There has been much discussion and sometimes heated debate about outcomes for children of color placed transracially and the ability of parents to address adoptees’ racial or ethnic socialization needs. Asian children come to U.S. adoptive families largely through intercountry adoption and their foreignness has been situated as a matter of culture rather than race both in practice and in the literature. A small sample of 68 families, where at least one parent is of Asian American descent, provided some preliminary insight into parental motivations and perspectives about adopting Asian children. The majority of the adoptive parents were Chinese American, and the children from China. The primary motivation for adopting was infertility, and similarity or “fit” led the parents to adopt Asian children. The parents overwhelmingly believed that having at least one Asian American parent would be easier for the children, referencing themes of acceptance, fit, and identity congruency. Implicit was their belief that racial and/or ethnic socialization strategies were less intentional and more “natural” than for white adoptive families. While the findings cannot be interpreted as conclusive or representative, overall respondents reported race and ethnicity to be salient to their adoptive decisions and strategies for developing their children’s identities.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".