The price of creativity: Policy and the professional artist in British Columbia
Bibliographic record
Abstract
The arts are a thriving business in Canada. The arts and culture sectors contribute 3% to the Canada’s GDP each year: a larger share than the agricultural, hospitality, or forest industries. However, professional artists—the core cultural labour force—are not as prosperous, and BC artists are worse off than most. The median income of artists in BC is the second lowest in Canada, well below the low-income cut-off. Although they are far more likely than the average worker to hold a university degree, BC artists earn an alarming 48% less than the provincial median for all workers. Women, visible minorities, and aboriginal people working as artists in BC earn even less.Using data collected from a jurisdictional scan, expert interviews, and an online survey of artists from across BC, I identify four potential policy measures to address the issue of low earnings in the arts sector. Options include an expansion of the existing project grants programs administered by the BC Arts Council, as well as three different plans to provide a monthly minimum income to artists. After analyzing each policy in terms of effectiveness, equity, budgetary cost, administrative complexity, and stakeholder acceptance, I recommend establishing a need-based but competitive grant stream providing a Basic Income to professional artists. Referring to survey data, I also propose a set of recommendations to enhance the accessibility, flexibility, and targeting of BC Arts Council programs.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.004 |
| Scholarly communication | 0.013 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".