Bibliographic record
Abstract
Beauty, Love and Justice Parisa Sabet Sarvestani Doctor of Musical Arts Faculty of Music University of Toronto 2019 Abstract “Beauty, love and Justice” is a five-movement orchestral composition approximately 16 minutes long. The orchestra consists of 2 flutes, 2 oboes, 2 clarinets, 2 bassoons, 4 horns, 3 trumpets, 2 trombone, 1 bass trombone, 1 tuba, percussion (4 players), 1 harp, 1 piano and strings. It is developed based on the basic musical elements that I found common among all cultures such as repetition of simple rhythmic pattern, harmonic series and natural sound effects such as breathing, humming, whispering, talking and air sounds. In addition, I explored various ways of incorporating unusual timbral effects and extended techniques to create an intriguing sonic environment. My ultimate goal was to create complexity by fusing simpler musical elements. The first movement begins with successive crescendos over repetition of simple rhythmic patterns; harmonic language derived from a harmonic series on “C”. As the grand opening leads into a calmer section, air sounds in combination with whispering percussive syllables “ta ka ta ka ta ka” express a longing to speak up. These sounds gradually become louder and thicker until the movement ends with shouting. The second movement starts mysteriously; the string section plays excerpts from a ‘fandom’ melodic line which is never presented in its entirety. In the meantime, a complex rhythmic pattern gradually reveals from the percussions section that leads to the ending that is similar to the first movement. The third movement is slow led by pitched percussion instruments while wind and brass sections play secondary roles. The fourth movement starts with percussion and winds on identical melodic material with that ending the third movement, but in a faster tempo. As it comes to the end, the same melodic materials is heard once again, faster this time, and builds up to a grand conclusion. Similar to the previous movements, the fifth movement starts exactly the same way as the first movement. However as the grand opening leads into the calm section, a well-known chant in the Baha’i tradition is leaping from one instrument to the next while the rest of the orchestra plays the original musical materials introduced in the first movement.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.088 | 0.029 |
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".