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Record W4223983778 · doi:10.3138/cjfs-2020-0011

Korean Financial Thrillers: Neo-liberal Governmentality in <i>Default</i> and <i>Black Money</i>

2022· article· fr· W4223983778 on OpenAlexvenueno aff
Jaecheol Kim

Bibliographic record

VenueCanadian Journal of Film Studies · 2022
Typearticle
Languagefr
FieldEconomics, Econometrics and Finance
TopicCinema and Media Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical sciencePost colonialismArtColonialism

Abstract

fetched live from OpenAlex

Cet essai vise à étudier les thrillers financiers coréens produits à la fin des années 2010, en particulier Default (Kook-hee Choi, 2018) et Black Money (Ji-yeong Jeong, 2019). Ces deux films ont dévoilé le visage sombre du capitalisme financier — non seulement en démystifiant les difficultés existantes et les défis causés par la crise du crédit, mais aussi en analysant l’ordre social du monde néolibéral actuel. En Corée, le néolibéralisme s’est développé après la crise financière asiatique de 1997 et il a été justifié par le fonds de sauvetage du FMI reçu par le gouvernement coréen pour échapper au défaut souverain. Les thrillers financiers coréens examinent le capitalisme mondial d’un point de vue nationaliste, et ils developpent un récit anticolonial pour representer la formation sociale néolibérale comme un type de régime colonial. Néanmoins, leurs points de vue ne se limitent pas à une portée nationaliste ; ils dêfinissent la logique d’exploitation actuelle comme une logique distinctive qui s’écarte de celle des anciennes relations coloniales. Ils comprennent l’économie néolibérale comme une gouvernementalité qui peut déplacer la souveraineté nationale, et ils imaginent des contre-conduites potentielles contre elle.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.374
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.030
GPT teacher head0.224
Teacher spread0.194 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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