Mythological Themes in Iranian Culture and Art: Traditional and Contemporary Perspectives
Bibliographic record
Abstract
In Iran, ancient mythical elements are very much alive in the present as a part of the fabric of ordinary people's lives and worldview. This paper explores the relationship between culture, myth, and artistic production in contemporary Iran, using the specific examples of symbols and mythological themes evoked in the work of painter/writer Aydin Aghdashloo and photographer/video artist Shirin Neshat. The paintings of Aghdashloo, in which he deliberately damages beautifully-executed classical style Persian miniatures, convey a sense that the angelic forces have failed and that the world is succumbing to the destructive and degenerative activities of the demonic. The photographs, videos and installations of Neshat likewise draw heavily on cultic forms inherited from ancient Iranian tradition. It is important to note that in none of these cases does the artist use mythological themes and symbols to express their original cultural meaning; rather, they appropriate well-known elements of ancient Iranian culture and imbue them with new meanings relevant to contemporary issues and understandings. What these examples do illustrate is the persistent resonance of ancient Iranian culture among Iranians up to the present day. Iranian artists have demonstrated the effectiveness of evoking their target audience's deep sense of cultural identity to convey contemporary messages using ancient cultural concepts, sometimes on a subconscious level.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.009 | 0.045 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".