Artist-Run Archives: Developing a digital community archive with PAVED Arts artist-run centre
Bibliographic record
Abstract
This case study details my partnership with PAVED Arts, an ARC in Saskatoon, Canada dedicated to supporting photography, audio, video, electronic, and digital arts. In this presentation I discuss how we designed a usable digital archive that serves PAVED's organizational culture and core uses. As an information technology librarian, I bring my IT and information management expertise to this collaboration with the PAVED staff and wider community to assess, organize, preserve, and provide access to their multi-format archives. Using community archiving and participatory action research methods, we focus on designing and implementing a digital archive that serves the needs of the PAVED community and is sustainable for this non-profit organization. Canadian artist-run centres (ARCs) are vital experimental and emerging contemporary art galleries and production centres that serve researchers, curators, artists, and the public. Many of these non-profit ARCs have been active for over three decades, accumulating significant records about their history, artist-run culture, and the work they show and produce. Because this experimental work is rarely documented elsewhere, ARC collections can be the most authoritative source for this information. While some ARCs have digitized records, most have kept physical documents in file cabinets, basements, and storage units, making them effectively inaccessible and vulnerable to damage and loss. With a focus on supporting emerging and experimental work, but lacking a mandate to preserve and provide access to their archives, ARCs are in the difficult position of having valuable collections that they want to share, but lacking the resources needed to do so.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.027 | 0.013 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".