Bibliographic record
Abstract
This overview concentrates on newer studies on art markets and on research that has not yet received attention in this context. This is not a résumé of recent art market research in general; rather, it is limited to the greater Renaissance. That is why Italy and the Low Countries are the areas that are of central importance. Of late, interest has centered on cultural transfer, that is, the diffusion and reception of the Renaissance and humanism in, and even beyond, Europe. In addition, studies of the Renaissance emphasis its social and economic location and, thus, the question of patrons, the art market, and the demand for art. Although this field has established its own research tradition in the past fifteen to twenty years, the dominant scholarship, stressing intellectual history or aesthetic approaches, has not taken adequate account of the development. Various sociological approaches have been tested for the reception of Italian Renaissance painting. We may distinguish here between macro-sociological, micro-sociological, and (macro-)economic approaches. The macro sociologists study the development of European society and associate particular artistic phenomena, such as the Italian Renaissance, with it. The micro-sociological approach, in contrast, places the material conditions under which art was produced at center stage. This approach includes, among others, the study of painters’ training, guild organization, and the relationship between painter and patron. Finally, the economic approach seeks to examine the links between a period’s artistic production and economic cycles. In all three approaches, the art market plays a role, although the focus could be on production and reception of art.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.252 | 0.043 |
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".