The glow of grime: Why cleaning an old object can wash away its value
Bibliographic record
Abstract
Abstract For connoisseurs of antiques and antiquities, cleaning old objects can reduce their value. In five experiments (total N = 1,019), we show that lay people also often judge that old objects are worth less when cleaned, and we test two explanations for why cleaning can reduce object value. In Experiment 1, participants judged that cleaning an old object would reduce its value, but judged that cleaning would not reduce the value of an object made from a rare material. In Experiments 2 and 3 we described the nature, age and origin of the traces that cleaning would remove. Now participants judged that cleaning old historical traces would reduce the object’s value, but cleaning recently acquired traces would not. In Experiment 4, participants judged that the current value of an old object is reduced even when it was cleaned in ancient times. However, participants in Experiment 5 valued objects cleaned in ancient times as much as uncleaned ones, while judging that objects cleaned recently are worth less. Together, our findings suggest that cleaning objects may reduce value by removing valued historical traces, and by changing objects from their historic state. We also outline potential implications for previous studies showing that cleaning reduces the value of objects used by admired celebrities.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".