Repatriation in university museum collections: Case studies from the Phoebe A. Hearst Museum of Anthropology
Bibliographic record
Abstract
Abstract University-based anthropology museums are uniquely positioned to pursue nuanced decisions concerning the disposition of collections in their care, setting best practice for the field. The authors describe a three-staged approach to repatriations that they led during their concurrent service as head of cultural policy and repatriation (Jordan Jacobs) and director (Benjamin Porter) of the University of California, Berkeley’s Phoebe A. Hearst Museum of Anthropology between 2015 and 2019. Examples involving human remains and cultural objects from Australia, Canada, Democratic Republic of the Congo, Iraq, Japan, Mexico, Panama, Peru, Saipan, Senegal, Vanuatu, Venezuela, and South Carolina in the United States demonstrate the benefits of transparency, open communication, and rigorous investigation of provenance and provenience, which may or may not lead to transfer based on the criteria and priorities of potential recipients. This article also provides a history of the Hearst Museum’s Cultural Policy and Repatriation division, which was disbanded in 2021.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.048 | 0.012 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".