Bibliographic record
Abstract
It is often thought that the boundaries and properties of art-kinds are determined by the things we say and think about them. More recently, this tendency has manifested itself as concept-descriptivism, the view that the reference of art-kind terms is fixed by the ontological properties explicitly or implicitly ascribed to art and art-kinds by competent users of those terms. Competent users are therefore immune from radical error in their ascriptions; the result is that the ontology of art must begin and end with conceptual analysis. Against this tendency towards concept-driven ontology, I offer a trio of objections derived from: (1) the cultural and temporal variability of concepts of art, (2) the systematic tendency, on the part of would-be ontological assessors, to err on the side of familiar categories or, conversely, to exaggerate minor differences between familiar and unfamiliar practices, and (3) the influence artworld precedents exert over expert and folk concepts alike. These considerations, I argue, mandate an epistemic humility that is simply unavailable to the concept-descriptivist.
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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 | 0.035 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.066 |
| Scholarly communication | 0.013 | 0.030 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".