Mind the gap: causes and consequences of the racial gap in music funding organizations
Bibliographic record
Abstract
This article explores the challenges of applying for and receiving music funding for Canadian music workers identifying as Indigenous, Black, or as a person of colour (IBPOC). Using survey data from the Canadian Live Music Association we find evidence of a racialized gap in music funding. Interviews with IBPOC music workers reveal the causes and consequences of the racial funding gap: there are limitations to worker agency wrought by gaps in knowledge of funding opportunities; perceived bias in favour of majority White-racialized music genres; and bureaucratic contradictions in funding applications that disadvantage IBPOC applicants. We argue that the conceptual tenets of racialized organizations and theories of racial inequality in cultural policy and creative organizations explain why gaps in funding persist, despite stated efforts to the contrary. We conclude by outlining a model of how music funding organizations exemplify racializing processes and offer policy implications for scholars and practitioners of arts funding.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".