Bibliographic record
Abstract
One of the standard ways for a critic to set up an argument is to state that the popular image of an artist is misguided. The rhetorical manoeuvre cuts two ways, or would were it not so overused. It sets the critic up as a thoughtful expert and clears the ground for the construction of a contrary image that claims to be truer to life. What remains for both the straw man and the thoughtful expert is the necessity of the image, a narrative trope that tags the artist with certain identifying traits and provides a ready means of orientation for both apprehending the artist's work and communicating about it in social contexts. It would be easy to dismiss these images as packaging or window-dressing, both of which they are, but it would also be a mistake. The images are as unavoidable as they are useful, the basic coinage of the pragmatics of art. They are also symptomatic of the cultural trends that they serve or challenge. None of them should be believed, exactly, but all of them should be taken seriously. This is perhaps especially true with respect to Haydn, whose fortunes, at least in the English-speaking world, have been tied exceptionally closely to a pair of images with remarkable staying power.
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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 | 0.002 |
| 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 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".