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Record W3162182643 · doi:10.1515/sem-2018-0096

Semiotic hybridization in Persian poetry and Iranian music

2021· article· en· W3162182643 on OpenAlexaff
Amir Sedaghat

Bibliographic record

VenueSemiotica · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicIslamic Studies and History
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSemioticsPoetryLiteratureRhymeLinguisticsMusicalPhrasePhilosophyArt

Abstract

fetched live from OpenAlex

Abstract This article demonstrates how Iranian classical music and Persian medieval poetry, taken as separate semiotic systems, form together, in certain contexts, a single hybrid semiotic system with overlapping structural features and shared aesthetic principles. Hjelmslev’s description of connotative semiotic systems serves as a theoretical framework to show the modalities of this hybridization. This phenomenon can be observed through comparative analysis of the interdependence of poetry and music in the Persianate World from a semiotic point of view. On the one hand, the quantitative (chronemic) meter of the Persian classical versification, called ‘aruz , as well as its extraordinarily heavy use of rhyme display a formal structure that evokes that of a musical phrase. On the other hand, the dependence of the structure of the Iranian musical system upon the rhythmic structure of classical poetry suggests a unique character of which few examples exist. This interdependence manifests itself particularly in the mystic (Sufi) poetry of the medieval period, specifically in the work of such renowned poets as Rumi, Sa’di Shirāzi, and Hāfez, who are among the best-known in the West. Examples of their lyric works are examined here to demonstrate occurrences of the collusion between the two semiotic systems.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.588
Threshold uncertainty score0.546

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.259
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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