Leveraging Information Systems for the Conservation of the Niki de Saint Phalle’s Tarot Garden Artistic Legacy
Bibliographic record
Abstract
Abstract. This paper presents a Geographic Information Systems (GIS)-based digital framework developed to support the long-term conservation of the Tarot Garden (Il Giardino dei Tarocchi), a monumental sculpture garden in Italy created by French-American artist Niki de Saint Phalle. While GIS is widely recognized as a powerful tool for organizing and analyzing heritage data, this paper emphasizes that its complexity can limit its accessibility and effectiveness in conservation planning. To address this, the project prioritized stakeholder usability, data interoperability, and capacity building. The work was conducted by the Carleton Immersive Media Studio (CIMS) in collaboration with the Getty Conservation Institute (GCI), the Niki Charitable Art Foundation, and the Tarot Garden Foundation, under the GCI’s Modern and Contemporary Art Research Initiative (ModCon). The initiative produced a suite of digital assets – including ortho-rectified images, measured drawings, and a photographic record portfolio – integrated into a GIS platform. Developed through an interdisciplinary approach involving conservators, documentation specialists, and site stakeholders, the system supports condition monitoring, comparative analysis, and evidence-based decision-making. By embedding digital tools into conservation workflows and training local staff in their use, the project fosters sustainable stewardship and helps preserve both the tangible and intangible heritage of the Tarot Garden.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".