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Record W2910736666 · doi:10.20495/tak.43.4_419

The Development and Social Background of Overseas Chinese Studies in North America:

2006· article· en· W2910736666 on OpenAlexaboutno aff
園田 節子

Bibliographic record

VenueTōnan Ajia Kenkyū/Tonan ajia kenkyu · 2006
Typearticle
Languageen
FieldEngineering
TopicMilitary Technology and Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

The field of Overseas Chinese studies is inextricably linked with the historical and social context of the nation-state in which the field was established.In discussing the development of historical studies of the Chinese in North America in the second half of the 20th century, this paper examines how Overseas Chinese studies was established as a specific research field and reveals the field's characteristics which are tied with the American context as a whole.From the early 1960s, Chinese immigrant intellectuals in the Canadian and American West Coast authored histories of the overseas Chinese in Chinese.These studies relied on the historical materials of Chinese immigrants and on Chinese secondary sources published under the Overseas Chinese policy of the Taiwanese KMT.From the 1970s, as part of the Asian-American movement, second generation and immigrant middle-class Chinese intellectuals established the new framework of Asian-American studies.This field proposed a scholarship which legitimated the historical experience and presence of the Asian in American society and was thus premised on Asians as American citizens.The most recent scholarship on the overseas Chinese has introduced the concept of transnationalism, which is premised on mobility, and several empirical historical studies have been produced in this field to overcome the nation-state paradigm.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.213

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0120.011
Scholarly communication0.0060.003
Open science0.0000.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.245
Teacher spread0.227 · 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 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
Published2006
Admission routes1
Has abstractyes

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