Theories, Methods and Testbeds for Curation and Preservation of Digital Art
Bibliographic record
Abstract
This paper presents the activities and first results of a case study-based research on preservation of digital art, with an overview of key challenges surrounding the creation, management and longterm accessibility of digital art and investigation of experimental testbeds to tackle these challenges. An outline of the results of the onsite visit conducted at the pioneering ZKM Media Museum is also provided. So far, the theoretical aspects of the problem of digital art preservation and curation have been examined without much grounding in experimentation, and not responding to the theoretical and methodological dilemmas posed by digital art (e.g. transience, emergence, and lack of fixity). One of the reasons for this is that research in digital art requires an experimental testbed in which to examine the implications of different preservation approaches and the impact they have on the works of art themselves. The goal of this investigation is to develop a theoretical framework against which languages and their notational systems proposed for preserving digital art can be evaluated.
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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 | 0.033 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.027 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".