Bibliographic record
Abstract
Any study of Haydn's posthumous reputation must wrestle with several key problems. To start with, it may appear that Haydn's reception over the century following his death has little relevance to our engagement with the composer and his music: an essay on this topic might therefore seem to offer merely a stroll through the byways of nineteenth-century journalism and belles lettres , of marginal importance to our picture of Haydn. Yet many of the perspectives that emerged in the nineteenth century continue to shape popular perceptions of the composer. Indeed, Jens Peter Larsen has argued that it is an exaggeration to speak of a modern image of Haydn, as opposed to a diluted variant of earlier views. If Larsen is correct, it might seem that in exploring this topic, our task is to unmask the distortions that accrued over the course of the nineteenth century, to assert our own truths against earlier fallacies. Certainly, this impulse has motivated most studies of Haydn's reputation. For Leon Botstein, the “stubborn veneer of the nineteenth century” must be “dissolved and scraped away” if a new picture of Haydn is to emerge; similarly, in 1935 Adolf Sandberger claimed that “one of the most important artistic tasks of the present” was to discard earlier images of the composer in order to “re-establish the full truth.” But, as is evident from Sandberger's study, such attempts to assert the unadorned truth add their own layers of ideological veneer to the picture of Haydn.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 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".