Bibliographic record
Abstract
The authors conduct an empirical investigation of the national Equity X Program to understand not just the barriers that women face in becoming producers and developing business skills to move their career forward as artist entrepreneurs in Canada but also how emerging professionals feel about their perceived opportunities. The authors first situate the study within the literature on equity and inclusion in the creative sector and the importance of the music industry in this context. They then use an impact assessment framework that incorporates key indicators around equity and inclusion, aesthetic goals and approaches, and accessibility measures to help organize six rich clusters of data drawn from respondents (N=397). Using iterative open coding of open-ended responses to semi-structured questions, as well as critical discourse analysis, the authors examine the clustered data to illustrate or address these impact measures and to tease out the implications of each cluster of data. The analysis of the six clusters of data can help the sector (and their funders and policymaking supporters) better understand priorities for entrepreneurial capacity-building, including professionalization, technical skills, self-development, a sense of belonging through networking, entrepreneurial development, and emotional engagement in the sector. While the study does not suggest that systemic discrimination can be overcome any time soon, it does provide evidence of the ways in which women creatives in music believe that becoming more proficient in technical and business skills will help them; it also illustrates their optimism about being able to overcome discrimination. This generates a more complex understanding not just of some of the challenges faced by diverse women artist-entrepreneurs within Canada’s music ecosystem but also perceptions about how they aim to overcome these challenges. The article concludes by outlining future work required to ameliorate system-wide discrimination in arts entrepreneurship.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.019 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.020 |
| Research integrity | 0.001 | 0.003 |
| 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 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".