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Record W2989336491 · doi:10.1080/17411548.2019.1686893

World cinema at Soviet festivals: cultural diplomacy and personal ties

2019· article· en· W2989336491 on OpenAlexaff
Elena Razlogova

Bibliographic record

VenueStudies in European Cinema · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicFrench Historical and Cultural Studies
Canadian institutionsConcordia University
Fundersnot available
KeywordsMovie theaterDiplomacyHollywoodMilitantNational cinemaLatin AmericansFilm industryMedia studiesState (computer science)Political scienceSociologyHistoryArt historyLawPolitics

Abstract

fetched live from OpenAlex

This article traces informal world cinema networks at Soviet film festivals. It argues that the cultural diplomacy approach, where state objectives determine the value of cultural exchange, fails to account for the full range of connections made at Soviet film festivals during the Cold War. Personal ties have been crucial to the development of film festivals and the cinematic movements they engendered. The Soviet state aimed to position Soviet cinema as a better alternative to decadent European and commercial Hollywood cinemas, and as a model for film cultures in socialist Eastern Europe and decolonization-era Asia, Africa, and Latin America. This article first demonstrates how the Moscow International Film Festival (1959-present) and the Tashkent Festival of Asian, African, and Latin American Cinema (1968–1988; Latin America included from 1976) constructed a more inclusive map of world cinema than major European film festivals at Cannes, Venice, and Berlin. It then shows how African, Cuban, and Vietnamese delegations forged informal alliances around the emergent Third Cinema (militant Third World cinema) movement at the 1967 Moscow festival. Strong unofficial connections formed by international festival guests transcended and contradicted the aims of Soviet cultural diplomacy.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.288
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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.087
GPT teacher head0.283
Teacher spread0.196 · 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
Published2019
Admission routes1
Has abstractyes

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