Loot, legitimacy, and provenance: Intermediaries and the legitimation of looted cultural objects
Bibliographic record
Abstract
• Provenance frameworks may enable trade of looted cultural heritage. • Conflicting understandings of “legitimate” provenance create a contested landscape. • Intermediaries in cultural institutions strategically use provenance to acquire loot. • The legitimacy of provenance is relationally constructed. The trade of cultural heritage operates at the boundary between legitimate and illegitimate spaces. While provenance is widely accepted as a barrier to the trade of loot, a growing body of work finds it may enable loot to enter legitimate institutions. This study builds on literature on legitimacy and grey markets to investigate how intermediaries in the antiquities market use provenance to legitimize looted cultural heritage. Drawing on interviews with curators, dealers, and auctioneers and scripting analysis of institutional records, the study identifies three "legitimizing provenance practices": an accumulative market setting that encourages collection at all costs; exclusionary professional networks that protect permissive practices; and everyday obfuscation techniques that mask looted origins. These practices operate within a polarized landscape of competing "pro-loot" and "anti-loot" applications of provenance. This reconceptualizes provenance as a relational social process shaped by competing value systems and unequal power relations. Interpreting legitimacy and provenance through the lens of “loot” demonstrates how such practices perpetuate colonial systems of dispossession in contemporary forms.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.073 |
| Scholarly communication | 0.018 | 0.027 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".