Potential Archives: Envisioning the Future of the Interdisciplinary Artist Archive in Canada (How Digital Humanities and Feminist Ethical Praxis Will Transform the Interdisciplinary Artist Archive)
Bibliographic record
Abstract
As digital media conservators Deena Engel and Glenn Wharton identify in the premise for the Artist Archive Initiative at New York University, conventional approaches to the artist archive neglect to study how the complexity of an artist's interdisciplinary creative practice can confound conventional archival systems and practices. My project demonstrates how artists' archives benefit from non-traditional archival methods that combine emerging digital archival strategies that accommodate and represent community networks and collaborations with the intervention of the artists themselves in the co-creation of accessible multimedia archives. This paper explores two main, preliminary ideas: why a transformation of the organization of artist archives is timely and important; and how digital methods and platforms have the potential to benefit artists, arts scholars, and arts archivists. Potential Archives is both a study and a framework, providing both a map of how these non-traditional methods have worked in the past, and a model for how to develop future artist's archives. My study and resulting framework will reconceptualize the interdisciplinary artist archive according to emerging feminist and digital epistemologies and methods to help artists plan for and prepare their future institutional archives and address emerging needs and concerns, while also assisting arts institutions in addressing such innovations.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.041 | 0.025 |
| Scholarly communication | 0.028 | 0.009 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".