Bibliographic record
Abstract
Much has been written about the presumed interaction between moral and aesthetic properties in art, about whether moral flaws in a work or its artist can compromise the work’s aesthetic value. In the philosophy of sport, similarly, the beauty of an athlete’s performance may be undermined by moral flaws in the performance itself (e.g., in a case of cheating). Yet to be addressed, however, is a potential analogy between artists and athletes where personal moral flaws failing to register in the work or performance may nonetheless compromise aesthetic response. Along with tracing the conceptual terrain in these debates and drawing on earlier work endorsing pluralism in such matters, I will argue that an athlete’s moral flaws may indeed compromise the aesthetic appeal of their performances, even where such flaws stand apart from those performances. In contrast to creative artists whose presence is immaterial to accessing their work, in the case of performing artists and athletes—since they themselves are the vehicles of their work—it is, and ought to be, harder to avoid having one’s moral response to the person diminish one’s aesthetic response to the work. We want athletes to be moral exemplars, I propose, less because they serve as role models and more because we want to preserve unspoiled the aesthetic rewards they provide.
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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.037 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".