Ivan Kozlenko’s Tanzher and the Odesa Myth: Multidirectional Memory As a Strategy of Subversion
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".