Bibliographic record
Abstract
Is ontologizing about art rightly held accountable to artistic practice, and, if so, how? Julian Dodd argues against such accountability. His target is “local descriptivism,” a meta‐ontological principle that he contrasts with meta‐ontological realism. The local descriptivist thinks that folk‐theoretic beliefs implicit in our practices somehow determine the ontological characters of artworks. I argue, however, that according a grounding role to artistic practice in the ontology of art does not conflict with meta‐ontological realism. Practice must ground our ontological inquiries because our task is to make sense of the practices into which artworks enter. Terms like ‘musical work,’ as employed by the ontologist, play an essentially explanatory role in this endeavor, and it is only in terms of this role that we can specify the object of our ontological inquiries. But neither our practices nor our folk beliefs are sacrosanct. In taking ontology of art to be reflectively accountable to artistic practice, I also reject Amie Thomasson's claim that it involves conceptual analysis and therefore cannot rightly claim to be directly revisionary of folk understandings. Ontology of art involves not conceptual analysis but the codification of a practice in a way that clarifies that practice.
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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.075 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".