Painting Women: Cosmetics, Canvases & Early Modern Culture; <br>Fashion and Fiction: Dress in Art and Literature in Stuart England; <br>John Donne's Poetry and Early Modern Visual Culture
Bibliographic record
Abstract
Early modern European visual culture-which means just about anything that was routinely or habitually visible in societies, from make-up to city-planning, from trade signs to page layout, from festivals to maps, from scientific illustrations to embroidered silks, from fine paintings to flower arrangement, from signs of gender, race, and class to heraldic insignia, from arithmetical charts to diagrams of new technology-was rich, broad, multiform, and omnipresent.Many recent works of scholarship draw on visual materials as adjuncts to their main argument, but the field of visual culture studies is relatively new; we can expect to see much more work in the area in the coming years.As a new and interdisciplinary field, the study of early modern visual culture does not include works that might be called cornerstones.It does have touchstones, however, and if I were asked for a brief list of these, I would include works from art history by
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 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".