Bibliographic record
Abstract
Over the dozen or so years of its existence the Artist in Residence (AiR) program at the Art Gallery of Ontario (AGO) has brought numerous emerging and established artists into the daily workings of the museum, inviting resident artists to explore and engage with the AGO’s collections, staff and public programs as they develop their projects. Support for a process of research-creation is fundamental to the opportunity offered by the residency. As a foundational component of the museum’s research infrastructure, the AGO’s Edward P. Taylor Library & Archives has played a key role in the residency program, allowing strategies of reading, citation and documentation to emerge as central themes in the cumulative body of residency projects, and allowing in turn for the possibility of project documentation to enter the archival record of the museum. Drawing on interviews with selected past artists in residence, this paper will provide an account of how the involvement of librarians and archivists, and the availability of library and archival resources in the museum have shaped the trajectory of the AiR program at the AGO.
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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 | 0.021 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.051 | 0.044 |
| Scholarly communication | 0.023 | 0.036 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".