Not Following the Book: A Journey from Museum Conservator to Digital Humanities Researcher
Bibliographic record
Abstract
This article explores the transformative power of innovation and collaboration in the realm of cultural heritage management, particularly focusing on the conservation and documentation of art exhibitions. Beginning with the creation of Novart, a relational database designed to integrate collection management and exhibition production, the narrative delves into the complexities of contemporary art conservation. It examines the fluid nature of exhibitions and the evolving landscape of conservation practices, emphasizing the need for adaptability and interdisciplinary collaboration. Through practical experiences in institutions like Luma Arles, where limitations of existing tools spurred the quest for innovative solutions, the narrative highlights the persistence of custom database development driven by the imperative to bridge the gap between conservation practices and digital innovation. Amid setbacks and organizational challenges, Novart emerges as a tool to streamline exhibition documentation and enhance museum operations, serving as a repository of institutional memory and information-sharing platform. Looking ahead, the narrative envisions a transition to academic research and a commitment to promoting interdisciplinary approaches in digital humanities. Proposing an innovative methodology for exhibition conservation, the thesis proposal reflects a deep commitment to preserving complex and ephemeral media for present and future generations. In essence, this journey encapsulates the spirit of innovation, collaboration, and perseverance essential for addressing the challenges of cultural heritage management in the digital age.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.017 |
| Scholarly communication | 0.027 | 0.024 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.018 | 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".