Aesthetic Properties of Everyday Objects
Bibliographic record
Abstract
This research addresses whether one underlying concept of appreciation exists across different classes of objects. Three studies were done. To identify aesthetic properties relevant for the aesthetic judgment of everyday objects and paintings, in Study 1 expert interviews were conducted with 12 interior designers, object-oriented designers and architects, and 12 students of art history. In Study 2, multidimensional unfolding (MDU) was used to examine whether common judgment criteria can be identified for the objects of the different classes. A sample of 217 German subjects participated. 2- or 3-dimensional MDU solutions resulted for each object class. The identified dimensions were labeled using the aesthetic properties derived from the expert interviews (Study 1). These dimensions represent relevant dimensions of aesthetic judgment on which object properties vary. Study 2 suggested that people use different dimensions of aesthetic judgment for different object classes. The identified dimensions were then used to construct three sets of systematically varied everyday objects and one set of systematically varied paintings. Using this stimulus material in Study 3, conjoint analysis indicated these dimensions are differentially important for the overall aesthetic judgment.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".