Bibliographic record
Abstract
This video and article are inspired by Robert Schumann’s use of characterization in his music and writings, and the affinities of these sometimes kaleidoscopic and multiple musical and critical perspectives with theatre, fragmentary scenographies, and costume. Drawing on the traits of Schumann’s own characters Florestan, Euesbius, Master Raro, as well as other personas such as Harlequin and Pierrot, I have aimed, in my audiovisual collage and performance of a hypothetical 4th movement to Schumann’s 1851 Sonata for Violin and Piano No. 1 in A minor (Op. 105), to create a mediation between disparate aspects of the music, envisioned as personality fragments scattered in the landscape of the piece. The video itself is a meditation on, and an outcome of the realities of the pandemic, and the isolation that intruded in such a sustained manner on my personal and professional life. The video is an impromptu assemblage of pre-pandemic footage and photography combined to create a collective virtual chamber music experience representing an internal world haunted by dissonant multiple characters. In some ways, I echo in performance certain suggestive strains of Schumann’s own creative imagination and his personifications, both musically and in his critical writings, of the characters Florestan and Eusebius whom he sought to amalgamate in the figure of Master Raro.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".