To Fit the New Art: 7 years of the Curating Art After New Media Curators’ Updating Course
Bibliographic record
Abstract
This publication is the celebration of a work-in-progress: the professional updating short course for international curators, Curating Art After New Media. As the course has been based on the generosity of many curators sharing their knowledge, the intent of this publication is to further share emerging new media art practices, and to discuss how curators can best fit their practices, so that audiences can engage with this exciting art. The one-week annual course ran in London 2014-20, and for obvious reasons, 2021 took the form of an online reprise. The course was instigated by Sarah Cook and Beryl Graham of CRUMB at the University of Sunderland, and was originally an off-campus section of the MA Curating course, also available to international curators. PhD students from the University also co-programmed the course each year. Course attendees have included curators and researchers from Hong Kong, Bahrain, India, the USA, Canada, Austria, the Netherlands, Greece, Ireland, France, and the UK, and have directly fed into impressive subsequent curatorial practice and projects. The organisations that we visited in London targeted a broad range of scales (Tate, Furtherfield), disciplines (Wellcome Collection, Iniva), and sectors (MachinesRoom, The Open Data Institute (ODI)). This strategy aimed to reflect the tendency of new media to cross many boundaries, where curators must also follow. This ongoing process is reflected in the nature of the document, i.e. this is more of a collection of notes and reflections on a currently developing field than an academic closed position. By making the publication available as a free PDF, we hope to continue to get feedback and comments, which will build upon this knowledge.
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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.061 | 0.036 |
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".