Reading between the lines in the art market: Lack of transparency and price heterogeneity as an indicator of multiple equilibria
Bibliographic record
Abstract
The hypothesis of a single deterministic price structure in the art market is unrealistic because of price dispersion , heterogeneity, limited information, and a lack of price transparency. Considerable price heterogeneity is associated with differences in quality; however, objective measurement of artistic quality is difficult, which reinforces the problem of lack of transparency in the art market. Applying finite mixture models to a sample of Surrealism paintings sold at auctions during 1990–2007, we test the hypothesis that the art market's lack of transparency is transferred to the art price system, which results in a fragmented market, characterized by the coexistence of different segments with various informational requirements, rules, and prices. Indeed, we find three distinct segments in the high end of the market, each with its own price structure. Furthermore, we identify a direct and an indirect effect on hammer prices exerted by the leading art auction houses.
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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 | 0.006 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".