Bibliographic record
Abstract
/ This article is concerned with the pioneering digital archive of media arts ANARCHIVE, which is supervised by Anne-Marie Duguet (Université de Paris 1 Panthéon-Sorbonne/University of New South Wales). Digitally archiving the works of media and video artists Antonio Muntadas ( Muntadas Media Architecture Installations, 1999), Michael Snow ( Digital Snow, 2002), Thierry Kuntzel ( Title TK, 2006), and Jean Otth ( Autour du Concile de Nicée, 2008), ANARCHIVE currently consists of one CD-ROM and three DVD-ROMs that include an important database of a given artist's oeuvre and that attest to the relational potential of digital archiving. Offering another type of digital archive than the ones found online, ANARCHIVE provides the user with what is arguably the most original form of digital archive today in the fields of contemporary and media arts. The rise of digital archives has accompanied a number of critical efforts that inquire into the ontological nature of the artefact once it has been `dematerialized'. More than a question of materiality or lack thereof, I wish to show that a project such as ANARCHIVE introduces another way of conceiving of cultural memory and digital preservation in the age of new media. Indeed, what such a project demonstrates is that relational aesthetics has come to occupy a more central role than materiality in the appreciation and preservation of cultural and media memories.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.063 |
| Scholarly communication | 0.021 | 0.022 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 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".