Repatriation in university museum collections: Case studies from the Phoebe A. Hearst Museum of Anthropology
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 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.
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 | 0.001 |
| 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) | 0.001 | 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