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Record W2030114166 · doi:10.1163/156916306777835277

Racialized Choices: Chinese Adoption and the `White Noise' of Blackness

2006· article· en· W2030114166 on OpenAlexaff
Sara Dorow

Bibliographic record

VenueCritical Sociology · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicChild Welfare and Adoption
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsWhite (mutation)ChinaWhite privilegeNormativeKinshipMulticulturalismGender studiesPrivilege (computing)SociologyRace (biology)Ethnic groupRacismPolitical scienceLaw

Abstract

fetched live from OpenAlex

The transnational, transracial adoption of children provides the opportunity to explore how race binds and differentiates kinship and national belonging, especially when considered in relation to options for adopting both at home and abroad. More specifically, the reasons white parents give for choosing to adopt from China reveal how the normative white, American family is constructed through discourses of foreign and domestic, Asian and black. I explore three themes that contribute to the relative desirability of adopting Chinese children: they are seemingly unfettered by attachments, racially “flexible,” and readily constructed as rescuable. In these discourses that bring Chinese children home, blackness serves as a mediating backdrop — a domestic “white noise.” But China — US adoption unsettles as much as it reproduces racial stratifications; ongoing encounters with intimate relations of difference push at the boundaries of white privilege and weak multiculturalism.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.012
Scholarly communication0.0030.002
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.011
GPT teacher head0.320
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations47
Published2006
Admission routes1
Has abstractyes

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