Digital Technology: the Answer to the Art Historical Conundrum of A ccessibility?
Bibliographic record
Abstract
The famous anthropologist Pierre Bourdieu argued that there is an “unnatural idea of inborn culture, of a gift of culture, bestowed on certain people by Nature.” [1] Bourdieu is arguing that people, who have not been born into a higher class, or who cannot receive a high level of education, are unable to appreciate and understand art. The study of art history is expensive, and often involves extremely high travel costs, thus making it inaccessible to anybody who does not enjoy the means to pursue it. How can we address this accessibility problem in the study of art history? Is there any way to bring art to the people who do not possess “inborn culture?” Bourdieu wrote his book on art and class in 1984, at a time when the computer, and its democratizing potential, was a new and little -understood invention. My research proposes that modern technology provides an answer to this problem, which has plagued the discipline of art history.
 
 This presentation will examine three research projects that I’ve been working on at Queen’s. Each project uses digital technologies to improve the general public’s knowledge and access to art. The projects are all different: the first focuses on creating a digital model of 18th - century Canterbury Cathedral based on a book from W.D. Jordan Rare Books and Special Collections, the second project works on understanding Herstmonceux Castle and medieval England through technology, and the third involves image processing for art historical investigations. Despite their differences, each project makes art accessible to people who do not possess Bourdieu’s definition of “inborn culture.”
 
 
 
 
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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; both teacher heads agree on what is shown here.
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".