Dead bodies: The changing treatment of human remains in British museum collections and the challenge to the traditional model of the museum
Bibliographic record
Abstract
The contestation over human remains in museum collections among indigenous groups, archaeologists, and museums that took place in the USA, Australasia, and Canada in the late 1980s developed more slowly in the UK. Law and codes of practise have now been passed to ensure the repatriation of human remains; the transfer to culturally affiliated groups is possible and accepted by the profession. This paper explores the influences on the construction of the contestation, to explain this development. Drawing on research for an ongoing study, this paper will first outline the influence of reparations thinking and a therapeutic ethos present in ideas in the politics of recognition. It is argued that the idea of human remains as a scientific resource holds less authority than the recognition of emotional claims for human remains from once colonized or disenfranchised communities. It is suggested that the museum profession has been receptive to claims for repatriation as a response to a crisis of legitimacy. Repatriation of human remains is part of a broader renegotiation of the basis of their authority. It is concluded that the traditional remit of the museum is questioned by these developments.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.019 | 0.045 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".