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Record W4307809011 · doi:10.21226/ewjus605

Ivan Kozlenko’s Tanzher and the Odesa Myth: Multidirectional Memory As a Strategy of Subversion

2022· article· en· W4307809011 on OpenAlexvenueno aff
Vitaly Chernetsky

Bibliographic record

VenueEast/West Journal of Ukrainian Studies · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEastern European Communism and Reforms
Canadian institutionsnot available
Fundersnot available
KeywordsSubversionNarrativePoliticsSociologyIndependence (probability theory)National identityHistoryAestheticsGender studiesLiteratureMedia studiesPolitical scienceArtLaw

Abstract

fetched live from OpenAlex

Ivan Kozlenko’s novel Tanzher (Tangier) became one of Ukraine’s biggest cultural events of 2017, vigorously debated in the country’s media and shortlisted for multiple prizes. This ambitious Ukrainian-language novel by a native of a predominantly Russophone city is simultaneously a love letter to Odesa and a daring subversion of the superficial version of the city’s popular myth, widely disseminated both by mass media and by scholarly discourse. A novel whose plot centres on two pansexual love triangles, one taking place in the 1920s, the other in the early 2000s, Tangier employs strategies of intertextual engagement and multidirectional memory to construct an alternative affirming narrative. It focuses on the episodes in Odesa’s history during Ukraine’s wars of independence in 1918–20 and the time it served as Ukraine’s capital of filmmaking in the 1920s and seeks to reinsert this queer-positive narrative into the national literary canon. This article analyzes the project of utopian transgression the novel seeks to enact and situates it both in the domestic socio-cultural field and in the broader contexts of global countercultural practices. It also examines the challenges faced by post-communist societies struggling with the new conservative turn in national cultural politics.

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)0.003
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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)0.0000.000

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 head0.050
GPT teacher head0.324
Teacher spread0.273 · 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 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
Published2022
Admission routes1
Has abstractyes

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