The Façade of Neutrality: Unearthing Hidden Histories in the Montias Database with Digital Methodologies
Bibliographic record
Abstract
Technological innovations and the 2020 global pandemic have increased scholarly interactions with and reliance on digital resources, leading cultural heritage institutions to prioritize initiatives that digitize and organize their collections online, aiding in their accessibility to a wider global audience. Focusing on the Montias Database of 17th Century Art Inventories, this article turns a critical eye toward digitized art-historical data that is collected, preserved, and organized by cultural heritage institutions. Using the digital art history project “Beyond ‘Exceptional’: Reassessing Women’s Participation in the Cultural Sphere of Seventeenth-Century Amsterdam with Digital Social Network Analysis,” I demonstrate the potential of computational methods for mediating the biases inherent to these collections.
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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 | 0.021 | 0.155 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".