Reputation laundering and museum collections: patterns, priorities, provenance, and hidden crime
Bibliographic record
Abstract
Provenance research in museums has traditionally been reactive and focused on singular objects with dubious histories, such as colonial-era acquisitions, Nazi-looted art, and objects with active ownership claims; the ‘crimes’ we expect to see. But what if what we think we know prevents us from seeing the bigger picture within and across museum collections? We argue that a machine-learning approach to provenance could allow the detection of broader patterns of unethical or even criminal behaviour that are embedded in the relationships underpinning museum collections. To demonstrate the potential of a machine-learning approach, we present a computer-assisted model that predicts plausible patterns and connections, ‘leads’ or ‘hot tips’, derived from a dataset of unstructured texts concerning the antiquities trade. Preliminary results have revealed what may have been a multi-decade scheme involving the donation of low-value Latin American antiquities to museums as a form of ‘reputation laundering’ potentially in advance of criminal fraud. We believe that such patterns could not be identified by an approach to museum provenance that is restricted to known problems within individual institution, demonstrating the need for innovative provenance tools and approaches that consider the complex networks within which museum objects exist.
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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 | 0.005 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| 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".