Bibliographic record
Abstract
Under the spectre of the Conservative majority government, and a generalized feeling that critical practices have been embraced by public and commercial galleries, much energy in artist‐run centres is diverted from programming toward negotiating the contradictory states of survival and growth. As if in response to the fear of closure, or of no future, many centres have become preoccupied with the state of their archives, or of historicizing their activities. What can be learned from the termination experiences of others? This Master Class will be led by Julie Ault, cofounder of Group Material. Active from 1979‐1996 in New York and internationally, this not‐for‐profit organization emphasized collaborative and socially engaged artistic practices. Drawing from her lived experience as a participant, and her later role as the ad hoc historian for the group as editor of Show and Tell: A Chronicle of Group Material (2010), Ault will lead non‐profit cultural workers in a day long reflection on the process of mourning the loss of an ideal. She will speak about the book as a form of exhibition space and the complexities of archiving and historicizing projects that evolved though multiple voices. Although there are differences between New York and Montreal artist‐run culture, Ault will bring her experiences to bear upon the local situation by speaking to shared acts of publishing, making projects, and the challenge posed by collective voices to the reductive archival principle of provenance.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.033 | 0.007 |
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".