Re-reading Poetry: Schubert's Multiple Settings of Goethe. By Sterling Lambert.
Bibliographic record
Abstract
Considering that Schubert wrote well over 600 songs, from his earliest adolescence to the end of his life, the term ‘career’ for his songwriting seems less than appropriate: it was, rather, a way of life. In 1815 alone, at the age of 18, he wrote about 150 songs, and the following year proved almost as prolific. We can follow his fascination with poetry from his earliest student years at the Stadtkonvikt, through the poems he set to music along with his involvement with the Bildungs circle, and later in life through his almost constant membership in reading societies, often led by his dear friend Franz von Schober. Schubert's commitment to poetry was unlike that of any of his musical contemporaries, in part because of the way he could connect personally with these texts, and one senses a heightened level of excitement when he discovered Goethe and started setting his poems in 1814.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.017 |
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".