Digital Records Management for Artist-Run Centres
Bibliographic record
Abstract
This poster will outline the ways in which Western Front Society, an artist-run centre based in Vancouver, BC, Canada, has developed and implemented digital preservation strategies, and how the work that has been done is applicable and beneficial for the broader non-profit arts sector in British Columbia and beyond. The first section of the poster will focus on the migration of digital assets to Collective Access, and how that system was customized to manage the Western Front archives, including digital assets (video, audio, images) and cataloging works and collections using the VRA Core principles. Then, the poster will outline the recent digital records management developments at Western Front, including email archiving using ePADD, web archiving using Conifer, and policy development, all of which is being designed to share with similar organizations dealing with arts and artists’ archives. This submission will be of interest for iPres attendees who work in organizations that struggle with funding and/or staffing resources, as it allows for an opportunity to speak about the challenges of small organizations dealing with idiosyncratic digital archives. Our work in this area began with a video digitization project and has grown into a website redesign, database migration, and a file server reorganization. The poster includes an embedded video from our archives, as well as a digital preservation workflow diagram and links to our website and an example of an archived web project in Conifer.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.011 | 0.001 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.014 |
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".