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Record W4255416502 · doi:10.32920/ryerson.14643858

Connecting the Uprooted: Parents' use of Bi-Cultural Socialization in Facilitating Transnationalism Among Chinese Adoptees

2021· preprint· en· W4255416502 on OpenAlexaffabout
Meaghan Symington

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicChild Welfare and Adoption
Canadian institutionsMcMaster UniversityToronto Metropolitan University
Fundersnot available
KeywordsTransnationalismSocializationEliteChinaGender studiesSociologyNonprobability samplingEthnic groupImmigrationEthnographyPopulationSocial psychologyPsychologyPolitical scienceAnthropology

Abstract

fetched live from OpenAlex

This Major Research Paper explores the distinct form of transnationalism experienced by Chinese adoptees in Canada by examining adoptive parents‟ use of bi-cultural socialization mechanisms. In doing so, this paper addresses the ways in which parents utilize cultural exposure to facilitate internal community ties and transnational connections between their children and China. The researcher attempts to present a link between parents‟ fostering of cultural knowledge and a resulting unique form of transnationalism that is not initially established or maintained through the efforts of the immigrant population (Chinese adoptee community). A qualitative research approach was undertaken through a purposive sampling technique, self-selection and elite interview data. Data was collected through in-depth one-on-one interviews with eight parents of adopted daughters from China. Through analysis of this empirical interview data and a theoretical reliance on the post-colonial paradigm of intercountry adoption, it was determined that Chinese adoptees in Canada experience and are attached to two or more places simultaneously.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.155
Threshold uncertainty score0.307

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.003
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0000.001
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 head0.049
GPT teacher head0.332
Teacher spread0.284 · 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 designObservational
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

Citations0
Published2021
Admission routes2
Has abstractyes

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