Bibliographic record
Abstract
Abstract This chapter examines the narrative and ethical influences of Russian realism (Fedor Dostoevskii, Lev Tolstoi, and Anton Chekhov) on New Turkish Cinema and on Iranian new wave cinema. While Turkish film directors Nuri Bilge Ceylan and Zeki Demirkubuz openly acknowledge their engagement with Chekhov and Dostoevskii, second wave Iranian auteurs Abbas Kiarostami and Mohsen Makhmalbaf and third wave directors Jafar Panahi and Asghar Farhadi engage with Russian realism more obliquely. Nineteenth-century Russia shares with late twentieth and twenty-first-century Turkey and Iran anxieties about art’s relationship to power as well as its ability to represent a world fragmented by rapid modernization. The chapter traces the historical and cultural parallels of the Russian, Ottoman, and Persian Empires, seeing them as the source for the similar preoccupations of Russian realist literature and Turkish and Iranian cinema. They share Dostoevskian themes of incarceration, philosophical nihilism, suicide, and power relations; Chekhovian themes of small-town loneliness and communication breakdown in human relationships; and Tolstoian visions of finding other ways of living on the periphery, as well as similar ideological situations like art’s resistance in the face of state censorship. The chapter shows how the aesthetic and ethical structures of Russian realism prove themselves to be surprisingly durable at the turn of the twenty-first century in the Middle East.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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.
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".