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Record W2803871498

Amesha Spenta for Two Pianos and Orchestra

2017· article· en· W2803871498 on OpenAlexaboutno aff
Iman Habibi

Bibliographic record

VenueDeep Blue (University of Michigan) · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Musicological Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPianoComputer scienceArtArt history
DOInot available

Abstract

fetched live from OpenAlex

Amesha Spenta is a 9-minute musical composition for two pianos and orchestra (2fls., 2cls., 2obs., 2bsns., 2hns., 2tpts., tbn. b. tbn. , timp., perc., and strings). It incorporates the Zoroastrian story of creation as its narrative framework and is the account of a struggle between the Zorastrian supreme deity, Ahura Mazda, and its adversary, Angra Mainyu (the destructive spirit). Angra Mainyu, later referred to as Ahriman, is the oldest known portrayal of the devil in a sacred text. The Amesha Spenta (divine sparks) are six divinities, each possessing a different divine character, created by Ahura Mazda with the intention of protecting the world, and defeating the evil Angra Mainyu. There is a close correlation between the dualism of good and evil as portrayed in Zoroastrianism, and the concerto form utilized in the composition: in both, we see a struggle for dominance between the various forces present. This piece is on one hand an attempt to cultivate better understanding of the Zoroastrian culture particularly in the United States, Canada, and Iran, and on the other, demonstrates the interconnectivity of musical traditions as far East as India, and as far West as Greece. My research shows a clear relationship between Zoroastrian chant, which dates back to 1500 BCE, and its successors, Western plainchant, and the Quranic recitation. While the work doesn’t contain any direct references to Zoroastrian chant, it incorporates Persian folk melodies, and shares commonalities with descendants of the above styles. Amesha Spenta is written in an attempt to address important current global concerns, including the persecution and unjust treatment of ethnic and religious minorities in countries such as Iran, while bringing these issues to the attention of a morally divided North American society. It aims to find cultural and artistic commonalities between various ethnic groups, to unite the followers of these religions through the language of music, and to celebrate them for their diversity.

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 score0.764
Threshold uncertainty score0.966

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.093
GPT teacher head0.226
Teacher spread0.133 · 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
Published2017
Admission routes1
Has abstractyes

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