The state of diversity among leadership roles within Canada's largest arts and cultural institutions
Bibliographic record
Abstract
Purpose This paper aims to answer the following research questions: Does the Canadian Arts Summit's membership (i.e. Canada’s largest cultural institutions) reflect Canada's diversity? What is the state of diversity among leadership roles within Canada's largest cultural institutions when viewed through a geographical, gender and racial diversity, and intersectional lens? Design/methodology/approach Employing a geographic, gender, racial diversity and intersectional lens, the authors investigated the largest and most influential arts and cultural organizations in Canada ( n = 125) to examine their leadership diversity. The authors found that there is a disconnect between the diversity of Canada and the leadership representation among the largest arts organizations. The authors rationalize the management implications of a lack of diversity leading Canada's cultural sector. Findings The leadership of major arts organizations in Canada does not reflect the diversity of Canada's population. For example, among 125 Canadian Arts Summit organizations, only 5.7% of CEOs are racialized compared to 94.3% who are White. The findings show similar results for lack of diversity in the Artistic Director and Chair of the Board roles. Originality/value There is limited research using this methodology to investigate leadership diversity, especially in the arts and culture sector. This research can create a benchmark for the sector to improve the status quo. The value of this research aims to encourage policy actors and arts leaders to address diversity and inclusion within their organizations and the communities they aim to serve. This research provides the foundation for future studies exploring leadership diversity and representation in the Canadian arts sector.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".