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Record W7133041227

Symphony No. 2

2020· dissertation· W7133041227 on OpenAlexaboutno aff
Daniel Mehdizadeh

Bibliographic record

VenueTSpace · 2020
Typedissertation
Language
FieldArts and Humanities
TopicMusicology and Musical Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsSymphonyRhythmMode (computer interface)Sonata formSet (abstract data type)MusicalBalance (ability)The arts
DOInot available

Abstract

fetched live from OpenAlex

Symphony No. 2 by Daniel Mehdizadeh Doctor of Musical Arts in Composition Graduate Department of Music University of Toronto © Copyright by Daniel Mehdizadeh 2020. Abstract Texture: One aspect of my approach is concerned with canonic layering of the various instruments and voices; I have explored these techniques throughout my doctoral degree and have further developed this method of layering (of motives and sustained notes) to create supporting texture throughout the symphony. Orchestration: I have abstained from the use of extended techniques to effectively showcase my ability to write concisely for the standard symphony orchestra. I have aimed for balance and clarity. This includes a clear approach to the canonic textures. Rhythmic development: One aspect of my music is to make use of ostinati patterns. I have been working toward breaking away from such habits, and to explore a continuous strain of rhythmic variation and development. I believe that this avoidance of ostinati grants me rhythmic independence. This symphony represents my attempt to synthesize these opposing rhythmic devices. Harmony: I have utilized my synthetic mode (the multi-tonic1 mode) along with Western traditional harmony, to ????nd a balance and a common-ground between the two. My multi-tonic mode has further set grounds for motivic variation, as well as harmonic structure. Motivic development: My dissertation utilizes a uni????ed motivic development throughout the entirety of the symphony. This includes secondary motives and “new” ideas all of which have been derived from the initial motif, which is rather simple and child-like in nature. Motivic contrast is often initially presented in a traditionally Western art music fashion and can subsequently be restated in the light of my mode. Form: This symphony is in 3 movements; fast-slow-fast. Within each movement the approach of through-composed has been employed. 1 Mehdizadeh, Daniel. (2018). Modality in Mehdizadeh. DMA Research Paper 2 of 2, submitted as partial ful????llment of the DMA in Composition Comprehensive Examinations, University of Toronto.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.454
Threshold uncertainty score0.778

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.4540.227

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 head0.031
GPT teacher head0.295
Teacher spread0.264 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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
Published2020
Admission routes1
Has abstractyes

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