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Record W3125701787

Age-Price Profiles for Canadian Painters at Auction

2011· preprint· en· W3125701787 on OpenAlexafffundabout
Douglas J. Hodgson

Bibliographic record

VenueRePEc: Research Papers in Economics · 2011
Typepreprint
Languageen
FieldArts and Humanities
TopicArt History and Market Analysis
Canadian institutionsUniversité du Québec à Montréal
FundersSocial Sciences and Humanities Research Council of CanadaSwenson College of Science and Engineering, University of Minnesota DuluthUniversity of Ottawa
KeywordsPaintingFunction (biology)EstimationArtHedonic regressionEconomicsEconometricsArt historyManagement
DOInot available

Abstract

fetched live from OpenAlex

We conduct an empirical analysis of the effect on the auction price of a Canadian painting of the age of the painter at the time of creation of the painting. We consider several hundred artists, active over the entire history of Canadian art, who are pooled in the estimation of a hedonic regression in which a polynomial function in age enters as a regressor along with several other control variables. We then consider the possibility that the age-price relationship has changed over time by: (a) estimating separate age-price functions for three generational groups of artists- those born before 1880, between 1880 and 1920, and after 1920 and thus coming of age in the world of post-war “contemporary art ” ; and (b) estimating a parameterization where the shape of the age-price profile is permitted to change continuously depending on the year of birth of the artist. Our principal result is that artists born more recently tend to “peak ” earlier in their careers than those of previous generations. As pertaining to artists born after 1920, this result is consistent with the findings of Galenson (2000) for modern American painters, but we find that the phenemenon applies over longer periods of art history, and propose an interpretation based on demand-side changes in art markets brought about by economic and urban population growth. 2

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.886
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.063
GPT teacher head0.273
Teacher spread0.209 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2011
Admission routes3
Has abstractyes

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