Bibliographic record
Abstract
A Beautiful Mind, is a 20-minute-long orchestral composition in two movements and for the following instrumentation: *2, *2, 2, 2—4, 2, 2, 1—timpani, 2 percussion; harp; and string orchestra. As the title suggests, A Beautiful Mind is an intuitive meditation on the phenomenon of Beauty and its roots in the human psyche. In my own musical experience, I have encountered this sense of beauty in the music of the Romantic era, early Impressionism and folk music of various traditions and moments in human history. In preparing for this composition, I spent quite a bit of time studying the music of European Romanticism. Among the many masterpieces of this era, I have been influenced the most by the colorful timbres and rich layers of Ottorino Respighi’s music and the long, languid continuous phrases in the orchestral works of Gustav Mahler. During the past ten years I have been living in Canada. It was my first extensive living experience outside China, my birthplace. During this time, I have been deeply influenced by the simplicity of the Canadian landscape, its people and their traditions. I find everything here simple and authentic. This connection between authenticity and simplicity is something I have discovered in my new country and have embraced it. Canada’s diverse culture and wide-open spaces have inspired in me a sense of originality and uniqueness that it is easier for an outsider to detect and be inspired by than for people born here who may be so used to it that it becomes invisible to them. Christmas Window, the first movement, was inspired by window displays in Toronto during Christmas time. Everything was so beautiful, and I felt overwhelmed by a sense of longing and beauty. Simple Elegance, the second movement, was inspired by a journey across Canada with my parents. They helped me synthesize our age-old traditions of my native China with the boundless beauty of my new country. A Beautiful Mind is dedicated to my parents and the love of my life, my husband.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 0.015 |
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".