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Record W2888865827 · doi:10.5509/2018912523

Christian Case for Engaging North Korea

2018· article· en· W2888865827 on OpenAlexvenueno aff
Joseph Yi, Joe Phillips

Bibliographic record

VenuePacific Affairs · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicKorean Peninsula Historical and Political Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGrassrootsFaithIndividualismGovernment (linguistics)Political scienceNormativeMainstreamSociologyLawPolitical economyPublic administrationGender studiesPoliticsTheology

Abstract

fetched live from OpenAlex

Abstract The Trump Administration’s strategy to isolate North Korea includes a ban on Americans travelling there. The 2017 ban especially impacts nearly seventy Christian faith-based organizations (FBOs), which in the past two decades legally channelled hundreds of (mostly volunteer) workers and thousands of tourists to North Korea. Since the travel restriction, these faith-based workers and tourists have publicly joined the debate over the United States’ North Korea policy. They have acted as “norm entrepreneurs,” shaping an alternative, cognitive frame for engagement. This frame includes three claims: 1) foreign Christian workers and tourists are generally tolerated by the regime, so long as they obey existing laws and regulations; 2) these workers and tourists meaningfully contribute to socioeconomic development and religious freedom in North Korea; and, 3) the US government should not violate the rights of Americans to travel and practice their faith, as long as they engage in these activities in a safe and meaningful way. The faith-based frame for peaceful engagement resonates among American mainstream and evangelical Christian media; it links with America’s individualist and evangelical normative traditions of social change through grassroots, personal relationships. We narrate and assess this frame through interviews and other communications with over twenty workers and tourists (mostly US citizens) linked with FBOs.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.986
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
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 head0.032
GPT teacher head0.290
Teacher spread0.258 · 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.

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

Citations8
Published2018
Admission routes1
Has abstractyes

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