Utopia and censorship: Iranian cinema at the crossroads of love, sex and tradition
Bibliographic record
Abstract
Abstract Iranian censorship forbids depictions of unrelated men and women touching one another. Given this, western scholars generally view Iranian cinema as poor territory for exploring love, sex and desire, with some even suggesting that pornography and Iranian cinema are two contrasting entities. Granted, it is difficult to find a direct representation of love and sex in Iranian movies but this limitation does not mean that Iranian cinema is devoid of such topics. In fact, as Hamid Naficy and Shahla Haeri both argue, Iranian directors have proposed very sophisticated, complex and ingenious methods for discussing eroticism, love and passion in their movies. When faced with strict censorship and social and moral barriers, what methods have Iranian directors developed in order to address love, desire and passion? In what ways do these methods emancipate or emasculate Iranian artists in their quest to express love and eroticism? This article attempts to answer these questions, arguing that it makes little sense to say that any authoritative system with a system of hegemony could prevent its citizens from expressing this impulse in their works since the sexual instinct is life’s drive and only at the moment of death can humans deny its existence. What is essential, radical and utopian is to read the meaning of eroticism in Iranian cinema through the specific culture in which the drive has been developed and shaped.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".