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Record W2727381817

Aesthetics vs Entrepreneurialism: Artists' Careers, the Market and Models for Art Business

2017· dissertation· en· W2727381817 on OpenAlexaff
Hayley Dawson

Bibliographic record

VenueOCAD University Open Research Repository (OCAD University) · 2017
Typedissertation
Languageen
FieldArts and Humanities
TopicArt History and Market Analysis
Canadian institutionsSchwartz/Reisman Emergency Medicine Institute
Fundersnot available
KeywordsThe artsScholarshipProfit (economics)Contemporary artCreative industriesAestheticsCultural economicsValue (mathematics)Art marketEntrepreneurshipArtBusiness modelRepresentation (politics)Art worldSociologyPolitical scienceVisual artsMarketingBusinessEconomicsComputer scienceNeoclassical economicsLaw
DOInot available

Abstract

fetched live from OpenAlex

This thesis investigates the contemporary art market in relation to the practices of working artists. The social mechanisms that determine artistic value influence the decisions and processes of all artists. Simultaneously, the economic structure of the art world is complex and perhaps challenging to navigate for those who are engaged in a creative practice. As a critique of the current economic systems that govern the art industry, two key questions are addressed through this research: How are financial and symbolic values attributed to art? How do profit-driven art institutions affect cultural production? Issues of artists’ rights, representation, and the feasibility of a career in the arts are also examined. By looking at commercially-oriented institutions such as auction companies in juxtaposition to the knowledge-based institutions of art scholarship, this thesis considers the theoretical and discursive logics at play in artists’ financial decision making and suggests options for combining aesthetics with entrepreneurialism.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0050.025
Scholarly communication0.0120.009
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0120.001

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.

Opus teacher head0.061
GPT teacher head0.269
Teacher spread0.207 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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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Same venueOCAD University Open Research Repository (OCAD University)Same topicArt History and Market AnalysisFrench-language works237,207