ARChives: Exploring the Community Archives of Canadian Artist-run Centres
Bibliographic record
Abstract
Artist-run centres (ARCs) are important cultural institutions and their archives form a unique record of artist-run culture and contemporary art in Canada. With mandates focusing on supporting new and experimental work, ARCs have not traditionally been records custodians; however the value of their collections and the growing need to preserve and make them accessible is undeniable. Considering the growing obsolescence of analogue media formats, the fragility of digital files, and the size of their collections, ARCs cannot delay planning for the preservation and use of their records or the task will outpace their capacity. This thesis investigates ARC archives by analyzing interviews with the directors of nine ARCs in Saskatchewan and Alberta, along with their online and physical archives, to identify the collection types, practices, and intentions of this rarely studied group. Applying theory and practice from the community archives literature, this thesis also identifies barriers ARCs face in making their archives accessible and proposes digital and collaborative solutions that fit with the organizational and operational culture of these non-profit, artist-run communities. Although robust, accessible digital archives are rare among the ARCs in this study, ARC directors express a growing interest in digital preservation and exposing their collections. This interest is attributable to a confluence of organizational age, a critical mass of records, and increasingly accessible technological solutions. Collaborations with established partner institutions, using a postcustodial model, is a solution that addresses many of the challenges ARCs face in managing their archives while staying true to their artist-run roots. These findings are specifically applicable to ARCs, but have implications for the preservation and access of cultural collections from similar community archives.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.024 | 0.009 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".