Bibliographic record
Abstract
This book began with the question: Are Canadian arts institutions gender equitable?There was good reason to be optimistic.After all, it had been more than forty years since women started protesting for greater representation in museums.Women have comprised more than 50 per cent of artists and 60 per cent of the art faculty in postsecondary institutions since the early 1990s, and women now occupy the majority of Canadian museum curator and director positions.Moreover, since gender issues have been so central to contemporary art practice, gender awareness seems a requisite part of any arts professionals' job.Mere numeric equity seems like an issue that the art world should have put to rest decades ago.We have shifted our focus to more complex considerations of sexual identities and politics, as indicated by the paucity of museum studies literature that seriously considers gender equity and by the focus on sexual identity rather than gender in much recent work. 1 Despite my initial focus on gender, I quickly realized the necessity of considering wider issues of representation in the study.Questions of racial or ethnic diversity have been important in both museum studies literature and museum practice since the 1980s.In Canada, the rethinking of diversity issues has been most productive with respect to Indigenous art, as reinstallations of the Art Gallery of Ontario (AGO) and the National Gallery of Canada (NGC) indicate.Innovative practices and a considerable body of scholarship have led to changes in many museums' relations with Indigenous peoples; yet the process of decolonizing museums and art galleries has not gone far enough.Despite years of study, many conferences, and symbolic gestures, the numeric record on Indigenous and other racialized artists remains
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.382 | 0.199 |
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".