The glow of grime: Why cleaning an old object can wash away its value
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it