Materiality, Making and Meaning: Building the Artist Record through Conservation in Indonesia
Bibliographic record
Abstract
Ways of knowing and understanding the artistic process are not fixed, and there are multiple perceptions that rely on the experience of the viewer and sources that inform them. this paper presents a case study of a conservation residency and collaborative treatment of indonesian artist Entang wiharso’s ‘landscaping My Brain’ (2001) oil on canvas triptych painting, to examine how we understand the artistic process from a conservation perspective and how this material knowledge contributes to the artist record. an interdisciplinary methodology for the conservation treatment of wiharso’s painting relied on technical and visual examination of the artwork in partnership with artist interviews and archival research. the residency concluded with an exhibition of the painting in an ‘active state of conservation’, highlighting the conservation decisionmaking process as value based and culturally grounded, leading to questions of authority, the role of technical-conservation expertise, what approaches work best, who should do the work and what knowledge informs it. in considering how we understand the artistic process, this paper will draw on the importance of practice-based interdisciplinary learning between conservator, artist, collector, curator and students, and the potential for collaboration and knowledge building at the intersection of these disciplines.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.022 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".