Bibliographic record
Abstract
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.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.454 | 0.227 |
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.
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".