Canadian Arts and Culture: Gender and Race in Leadership
Bibliographic record
Abstract
Representation in leadership is important not only because it often shapes the ways in which priorities are set and decisions are made but it also signals who belongs and shapes the aspirations and assumptions about what is possible. Extensive research across sectors has exposed the under-representation of women, racialized people, Indigenous peoples, persons with disabilities and members of the LGBTQ2S+ community in leadership roles in Canada. A range of initiatives have been introduced including legislation, voluntary codes, educational programs and procurement and funding priorities to improve representation. In Canada, the arts and cultural sector is significant not just because it employs more than 851,456 people and receives hundreds of millions of dollars in government funding but because it plays a profound role in shaping values and culture with impacts far beyond its boundaries. While in recent years, organizations have been under growing pressure from communities and funders to address diversity and inclusion, there is often a gap between statements and actions. This paper examines dimensions of diversity and inclusion in the largest and most influential arts and cultural organizations in Canada (n=125). First, it examines the extent to which they have expressed commitments or aspirations to diversity and inclusion. Then it examines the representation of women and racialized people in leadership roles. Finally, it recommends the elements of a comprehensive strategy, grounded in the critical ecological model, to promote change at the sectoral, organizational and individual level.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.027 | 0.010 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.000 |
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".