Toward a twenty-first-century model for the collaborative care and curation of human remains
Bibliographic record
Abstract
Currently, Canadian and U.S. museums and other repositories curate more than 175,000 North American Indigenous human remains. Despite these individuals’ great significance to descendant communities and potential value for substantive scientific research, many museums struggle to implement flexible and sustainable collections care procedures that meet contemporary curatorial standards. To address these issues, the Field Museum is piloting forward thinking, collaborative, and ethical curation, documentation, and physical care of the approximately 1,800 North American human remains housed in its collection. This initiative as part of a three-year Institute of Museum and Library Services (IMLS)-funded grant aims to build networks among scientific, museum, academic, First Nations, and Native American representatives. A symposium held with a number of partners from across North America has also shaped our approach through the dialogues on collaborative curation fostered both during and following the event. These discussions have provided the Museum with important themes, goals, and lessons, which have guided the subsequent course of the program, fostering community, providing resources, and promoting public outreach. To this same end, the Museum has implemented an updated and comprehensive human remains collections policy that covers all individuals held in the Museum’s collections.
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 | 0.013 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.045 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".