A Digital Archives Framework for the Preservation of Cultural Artifacts with Technological Components
Bibliographic record
Abstract
The preservation of artistic works with technological components, such as musical works, is recognised as an issue by both the artistic community and the archival community. Preserving such works involves tackling the difficulties associated with digital information in general, but also raises its own specific problems, such as constantly evolving digital instruments embodied within software and idiosyncratic human-computer interactions. Because of these issues, standards in place for archiving digital information are not always suitable for the preservation of these works. The impact on the organisation and the descriptions of such archives need to be conceptualised in order to provide these technological components with readability, authenticity and intelligibility. While previous projects emphasized readability and authenticity, less effort has been dedicated to addressing intelligibility issues.The research into the specification of significant properties and its extension, namely significant knowledge, offers some grounds for reflecting on this question. Furthermore, the relevance of taking into account the creative process involved in the production of technological components offers an opportunity to redefine the status of technological agents in the performative aspect of digital records. Altogether, the research on significant knowledge and creative processes provide us with a conceptual framework that we propose to bring together with digital archives models to form a coherent framework.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.021 | 0.023 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".