Bibliographic record
Abstract
Light Traveller is motivated by the works of Czech photographer Josef Sudek. Masterfully portraying contrasts of light and dark in his black and white photography, Sudek has been described as a “traveler in light” for his photography work. In creating this orchestral composition, I was interested in exploring compositional methods that aurally express how Sudek manages space, texture, and most specifically the use of light and dark variances in his photos. I consulted specific collections of photographs from his body of work. The subject materials in these photographs became metaphors that I explored sonically. Light Traveller is structured into four main sections: a beginning section, two middle sections, a final section that recapitulates ideas from the first section, and lastly a coda at the end of the final section. This work embraces measured and deliberate pacing, and is mainly concerned with exploring the range of timbre and resonance possibilities that the orchestra is capable of creating. The composition utilizes static harmonies and slowly unfolding harmonic changes. Quarter tones are employed as a means of expanding the colour and harmonic palette used for this composition. Differing textures are generated through intricate rhythmic materials, gradually moving materials, and static material.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.100 | 0.031 |
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".