Bibliographic record
Abstract
Abstract Intuitively speaking, a multiple artwork is one that admits of multiple ‘instances’ which are capable of playing a particular role in the appreciation of the work. The ‘explananda’ in the title of this article are things that have been proposed as requiring explanation by any adequate ontology of multiple artworks so conceived. This assumes that the ontology of art is in the business of explaining certain things, an assumption I defend. At least nine purported explananda have been proposed in the relevant literature. I begin by offering a preliminary sketch of these explananda, identifying how they are grounded in our ordinary artistic practice and discourse, and how they have structured recent debates in the ontology of art. I next argue that the notion of ‘instance’ must be understood in a particular way if instance multiplicity is to capture the standard distinction between singular and multiple art forms. I then assess the relative significance and implications of the nine explananda for an adjudication of the debates in the ontology of art. I identify problems for the historically dominant ‘type’ theory of multiples, and propose an alternative account that speaks to all nine explananda. I conclude by reflecting on where this leaves us and how we should proceed.
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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.036 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".