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Record W3123324095

Pride, Prejudice and Manchurian Heritage: North Korean Migrants and Memories of a Land Left Behind

2020· article· en· W3123324095 on OpenAlexfundno aff
Christopher Green

Bibliographic record

VenueLeiden Repository (Leiden University) · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicKorean Peninsula Historical and Political Studies
Canadian institutionsnot available
FundersPeople's Government of Jilin ProvinceUniversity of TorontoUniversiteit Leiden
KeywordsPridePrejudice (legal term)State (computer science)SocializationAgency (philosophy)PopulationEthnic groupGovernment (linguistics)EthnographyGender studiesPolitical scienceGeographySociologyLawDemographySocial science
DOInot available

Abstract

fetched live from OpenAlex

This thesis puts the relationship between the North Korean central state’s conservative official culture and variegated forms of real culture under the microscope. Via a survey and interview project conducted in 2016 that involved 1.5 percent of the resettled North Korean defector-migrant population in South Korea, the work thus interrogates the “cultural identities” of North Koreans. It finds that while the North Korean government promotes a vision of itself that is infamously racially charged and enforces ethnic group closure as a principle, ordinary North Korean people retain agency in their lives. Identities emerge that are based on much more than just the socialization processes imposed by the state from above in schools and other institutional settings.

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)0.001
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 score0.023
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.006
Scholarly communication0.0040.003
Open science0.0000.003
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 head0.014
GPT teacher head0.201
Teacher spread0.187 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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