Reactivating and Preserving Interactive Multimedia Artworks: An Analog Performance from the Seventies
Bibliographic record
Abstract
Interactive multimedia art shows a complex nature as it is time- and process-based, interconnected with technology, derived by the participation of several authors (artists, technicians, performers, to name a few) and an audience, and it is strongly tied to the moment and space of the original exhibition. These characteristics make the preservation of these artworks a multifaceted process. Building on the foundations developed since the 2000s by international projects focused on preserving and restoring these new art forms, the paper proposes an original model for achieving dynamic preservation, called “the multilevel preservation model”. Since it is no longer possible to guarantee physical integrity for interactive multimedia artworks, dynamic preservation involves recording all the changes that occur to display the artworks in the future. In other words, it makes it possible to record the dynamic authenticity of artworks. The model proposes two fundamental properties: multiple layering, which allows to handle different levels of information about the artwork; multiple belongingness , which allows to represent the dynamic authenticity of the artwork at the archival level and achieving dynamic preservation. The paper demonstrates the high-level implementation of the model by presenting a case study: the reactivation and preservation of an analog video art performance from the 1970s. This case study proposes an interesting real scenario for testing the model, as the reactivation process involved the migration of the entire analog technological apparatus of the artwork into the digital domain.
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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 | 0.001 | 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 | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".