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Record W2728851142

The price of creativity: Policy and the professional artist in British Columbia

2017· article· en· W2728851142 on OpenAlexaboutno aff
Dorothea Margaret Hayley

Bibliographic record

VenueSummit (Simon Fraser University) · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsnot available
Fundersnot available
KeywordsCreativityEconomicsPolitical scienceArtLaw
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.684
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.257
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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