Digital Art History: Moving Towards a Decolonizing Methodology
Bibliographic record
Abstract
This thesis examines current approaches to digital art history, while arguing for the importance of integrating decolonizing methodologies into digital platforms. As a case-study, the thesis analyzes how the artwork of Nadia Myre (an Algonquin member of the Kitigan Zibi Anishinabeg First Nation, b. 1974) appears on the websites of the Montreal Museum of Fine Arts, Art Mûr gallery and AbTeC virtual gallery. In this thesis, I draw on the visual theorist Johanna Drucker’s distinction between “digitized art history” and “digital art history,” as well as concerns raised by the art historian Nuria Rodríguez-Ortega about the techno-determinism surrounding digital art history – to construct a critical approach to the process of art history going digital. I offer decolonizing methodology as a way forward that implicitly answers these scholars’ calls for methodological complexity in digital art history. Referring to Linda Tuhiwai Smith’s influential writings on decolonizing methodologies, I use the concepts of “remembering” and “reframing” as decolonizing sub-categories, or strategies, as a theoretical framework through which to examine a range of digital platforms. Even though these sub-categories are not the only possible approaches to take, they are a compelling place to start investigating how digital art history resonates with older art historical methodologies, and how digital practices can transform art history in a critical way.
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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 | 0.041 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.008 | 0.070 |
| Scholarly communication | 0.019 | 0.028 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".