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Record W4401114949 · doi:10.22329/gljuh.v9i1.8865

A Tale of Two Motherlands: Bridging the Gap Between the American and Korean Identities of Korean War Adoptees

2024· article· en· W4401114949 on OpenAlexaff
Lily Zitko

Bibliographic record

Venue˜The œGreat Lakes journal of undergraduate history. · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicChild Welfare and Adoption
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsAmericanizationGender studiesPolitical scienceGovernment (linguistics)Race (biology)Spanish Civil WarSociologyLaw

Abstract

fetched live from OpenAlex

In 1955, Harry and Bertha Holt successfully petitioned for the passing of Private Law 475 (Holt Bill), allowing for the adoption of eight orphans from South Korea. This was the beginning of a global revolution in transnational and transracial adoption. Prior to this, the idea of adoption outside of the United States was seldom possible; however, the work of the Holt family rationalized with the pubic and garnered much attention from the government and media. Even more so complicated was the idea of mixed-race Korean children, fathered by American G.I.s stationed in the country during the Korean War. Their existence challenged conventional American views of race and hereditary purity. This paper aims to explore the story of Korean orphans in the United States. Moreover, it will attempt to further understand the process of “Americanization,” which these children were subjected to. The work will also consider the ways in which both the United States and South Korean governments handled these adoptions. Undeniably, the media played an important role in influencing not only the general public but also the images of the Korean orphans and their families, both biological and adoptive. Finally, this paper will analyze the long-term effects of transnational and transracial adoption on children, taking into account the research of scholars prominent in the field. This will include the study of identity-formation and cultural maintenance in relation post-war Korean adoptees.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.636
Threshold uncertainty score0.684

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.023
GPT teacher head0.275
Teacher spread0.251 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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