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Record W2493735311

Human Capital and Earning Differentials for Canadian Artists

2016· article· en· W2493735311 on OpenAlexaboutno aff
Laurence D. Dubuc

Bibliographic record

VenueE-Journal of international and comparative labour studies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsnot available
Fundersnot available
KeywordsHuman capitalEarningsLabour economicsCensusEconomicsInvestment (military)Human capital theoryThe artsOpposition (politics)Demographic economicsSociologyPolitical scienceFinanceEconomic growthPopulation
DOInot available

Abstract

fetched live from OpenAlex

Artists have traditionally been depicted in academic literature as younger and more educated workers who gain less economic returns from their human capital investment and earn significantly lower self-employment income than workers showing similar human capital features on more traditional labour markets. Filling an important gap in the literature relating to Canadian artists and their financial situation on the labour market, this article estimates the effect of human capital features and other socio-demographic variables on self-employment income of 9 categories of artists using data drawn from the Canadian census of 2006. Results show that experience is not generally associated with an increase of earnings for Canadian artists. Moreover, in opposition to human capital theory, a higher level of education is not consistently associated with higher earnings. Rather, it seems that only particular diplomas yield positively on earnings depending on artistic specialty. This indicates that there exist a few precise profitable profiles of education in each different field of the arts. Our findings constitute a major contribution in cultural economics while providing useful information to educational policy designers in Canada.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.510
Threshold uncertainty score0.956

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 studies0.0000.000
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 head0.122
GPT teacher head0.375
Teacher spread0.253 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2016
Admission routes1
Has abstractyes

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