Bibliographic record
Abstract
Artists have long engaged with digital and networked technologies in critical and creative ways to explore both new art forms and novel ways of disseminating artworks. Net-based artworks are often created with the intent to circulate outside traditional institutional spaces, and many are shared via artist-run platforms that involve curatorial practices distinct from those of museums or commercial galleries. This article focuses on a particular artist-run platform called Paper-Thin, characterizing the activities involved in managing the platform as digital curation in a polysemous sense – as both the curation of digital artworks and the stewardship of digital information in a complex technological ecosystem. While scholars and cultural heritage professionals have developed innovative preservation strategies for digital and new media artworks housed in institutional collections, the ongoing care of artworks shared through networked alternative spaces is largely carried out co-operatively by the artists and curators of these platforms. Drawing on Howard Becker’s sociological theory of art worlds as networks of co-operative actors, this article describes the patterns of co-operative work involved in creating, exhibiting, and then caring for Net-based art. The article outlines the importance, for cultural heritage professionals, of understanding the digital-curation practices of artists, as these artist-run networked platforms demonstrate emergent approaches to the stewardship of digital culture that move beyond a custodial paradigm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.013 | 0.048 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".