Bibliographic record
Abstract
The first of three art chapters describes deep-ken approaches to the problem of representation: Deep-ken art is beautiful and proportioned, indifferent to plain-ken particularities, and richly (literally, with expensive material) coloured. The chapter looks in particular at consonances between symbol and referent, between exemplars of a tradition, between parts and the whole, between the expense of the pigments and the awesomeness of the subject, and between the depicted and the context of the art. The fifteenth-century is one of the great turning points in Western art history, celebrated as a shift towards images more three-dimensional, natural, and realistic—that is, closer to the plain-ken spacetime we generally perceive as natural and real. Rather than talking about a shift from abstract to real, we might consider a shift from a deep-ken realism to a plain-ken realism. Each style looks real if the viewer has the appropriate perspective. Indeed, in eastern Orthodox societies, Christians who had religious visions could and did use these “abstract” icons to identify their apparitions. Looking at Renaissance art as a shift between two kinds of realism, from the deep ken to the plain ken, makes it easier to dispel, or even reverse, triumphalist accounts of Renaissance art as progress.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".