“What Sāz Did We Go to War with?” Musical Representation and the Possibility of a Persian Musical Modernity
Bibliographic record
Abstract
The works of prominent contemporary Iranian composers Hossein Alīzādeh and Parvīz Meshkātīān demonstrate a novel approach to the text-music relationship, characterized by a new understanding of the interaction between music's internal syntax and structure and the external world. This innovative approach, which can be described as “musical realism,” strives to represent textual meanings through musical gestures, particularly within the tasnīf genre, a metrical, pre-composed song with a pre-determined way of accompaniment, which developed over the past century.1 This approach, emerging primarily in contemporary compositions, is exemplified by the above two composers, who illustrate textual meaning through the intricate utilization of innovative treatments of modes, rhythm, melody, and texture relying on the potentials of the core of Persian musical tradition, the radīf – a collection of traditional Iranian melodic figures passed down orally through generations and serving as the foundational framework for improvisation and composition.2 By examining select instances from these composers’ works, I highlight the growing emphasis on a realist music-text relationship and its interplay with both the inner structures of Persian classical music and the broader context of Iran's modern position in the world.
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 | 0.002 | 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 | 0.005 | 0.025 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".