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Record W2329941583 · doi:10.5509/200881153

North Korean Market Vendors: The Rise of Grassroots Capitalists in a Post-Stalinist Society

2008· article· en· W2329941583 on OpenAlexvenueno aff
Andrei Lankov, Seokhyang Kim

Bibliographic record

VenuePacific Affairs · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicKorean Peninsula Historical and Political Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCommunismGrassrootsState (computer science)BoomUtopiaPolitical scienceEconomyPolitical economyEconomicsMarket economyPoliticsLawEngineering

Abstract

fetched live from OpenAlex

A communist utopia, the professed goal of all Leninist regimes, is incompatible with a monetary economy or commercial activities of any kind. Hence, the gradual displacement of commerce is seen as an important part of the transition to full-fl edged communism. However, no communist state has ever been able to eradicate private economic activities completely and despite their persistent efforts, all Leninist regimes have had to tolerate the existence of a “second economy.” In North Korea this “second economy” was originally treated with unusual harshness. The economic collapse of the 1990s, however, was marked by a powerful revival of the “second economy” and a boom in private commercial activities, known to the North Koreans as changsa (the literal translation is “dealings in the marketplace”). While comprehensive statistics on market activities in North Korea are not available (and perhaps do not exist at all), one is now able to obtain a wealth of information about this sector from North Korean defectors in South Korea. This article traces the changes as experienced by a group who were directly involved in market activity in the late 1990s. The authors have conducted in-depth interviews with North Korean defectors who now reside in Seoul. All interviewees can be described as fullor part-time market vendors whilst in North Korea. The unstructured interviews have allowed us to concentrate on issues with which a particular interviewee may be most familiar. However, refugee interviews can be biased, as Jung and Dalton have recently noted.1 Hence, this paper uses other material to corroborate the interviewees’ data—largely publications in the South Korean press. To protect the refugees’ identities, we refer to them using only their numbers. Some relevant information in regard to our interviewees is summarized in the following table.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.763
Threshold uncertainty score0.997

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 studies0.0010.002
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 head0.018
GPT teacher head0.247
Teacher spread0.229 · 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 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

Citations41
Published2008
Admission routes1
Has abstractyes

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