Bibliographic record
Abstract
Since the 1980s, museum professionals have increasingly committed to sharing collections with the descendants of people and communities from whom the collected artifacts originated. As late as the 1970s, Indigenous people were not considered stakeholders in the collection and exhibition of their own cultural artifacts. Recently, however, exemplary cases of collection sharing have occurred in North American and European museums. Museums have become “contact zones” as issues of decolonization have come to the fore. This article discusses the sharing of material culture and “double” position of anthropological museums, rooted in their own (colonial) history but in possession of another’s culture. Ownership issues, access, and ethics are important for local communities but not always easy for museums to negotiate. This article describes thirteen examples of collaborative partnerships between museums, for the most part large, urban, European, postcolonial institutions, and Arctic Indigenous communities. I argue that open communication, collection research, and an increasing level of co-curation are prerequisites for changes in museum practice, and these changes will benefit both the institutions and the communities involved.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.028 | 0.009 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".