Bibliographic record
Abstract
Artistic production in the long 17th century in the Dutch Republic radically reenvisioned the forms of visual culture and its consumption. In the wake of the Dutch Revolt of 1579 that severed the formerly conjoined Low Countries into the largely Catholic regions of Flanders, controlled by the Spanish Habsburgs, and the predominately Protestant Dutch Republic, which fought for the political independence that it officially achieved in 1648, Dutch art developed a distinctive, if not revolutionary, character. During the period, there was a multifaceted and unprecedented flowering of diverse secular genres, from still life to landscape to genre image to portrait. Each of these types of subjects had historical precedent, but they had not existed as independent genres complete with individuals who specialized in the creation of just one category of art. Many artists employed a highly naturalistic mode of representation when crafting these secular genres, as did those who produced the histories and biblical narratives that also remained popular. In paintings and prints, artists largely strove for naturalistic representations of space, volumetric renderings of objects, seemingly accurate depictions of light, and unidealized formulations of the human body, especially the face. As a result, audiences have frequently labeled 17th-century Dutch art as “scenes of everyday life.” The seemingly truthful appearance of these images has led to a complex body of literature that has proffered myriad interpretive schemata to understand the meanings of individual pictures, the genres of representation, and the aesthetics of naturalism. Like the new forms, art was consumed in way that it had not been previously. The relatively wide distribution of wealth in the Dutch Republic led to more people, and people of different social levels, buying art than had occurred previously in Europe. In turn, the consumption of art operated on an unprecedented scale. Several million new paintings were created in the region in a little over one hundred years. Aside from portraiture, few of these objects were created on commission. Rather, various indirect methods of exchange emerged, creating an open art market.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.005 |
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".