Bibliographic record
Abstract
In the 19th century, the museum was generally constituted as an accumulation of uncatalogued objects, while its fundamental role was relatively haphazard, with principal concern the elite’s good taste and high culture provided within a sacred site. At this time, heritage organizations began serving as a pedagogical source and incorporated learning strategies to accommodate the general public. Influenced by the Arts and Craft Movement, the Industrial Revolution brought an art education awareness, which first flourished in European museums, and then emerged after the Civil War in the United States—principally between 1870 and the Wall Street crash of 1929—for studying important artworks and supporting art appreciation through a constructivist perspective (Zeller, 1989). Some scholars posit that constructivism is the most convenient way to subjectively gain understanding, by involving visitors as active learners beyond the traditional approach (Hein, 1998). Earlier than the Second World War, the Metropolitan Museum of Art of New York (www. metmuseum.org) was already known as a leader for setting educational programs with unique behind scenes of major masterpieces (see Figure 1), whereas the Louvre in Paris (www.louvre.fr) rapidly acted as a model in the Victorian Era for other established museums throughout the continent. Both Web museums of these organizations have shown creative ways of displaying their contents and for attracting an international crowd.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.023 | 0.014 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".