Assembling Bodies: the Museum Exhibition as Apparatus of Bodily Production in Canada and the United Kingdom
Bibliographic record
Abstract
Museums have displayed archaeological human remains, alongside material culture, as a way of interpreting past human life for several centuries. Disciplines such as osteo-archaeology and physical anthropology have developed nuanced practices for analyzing and presenting knowledge about bodies alongside the project of natural and cultural history museums to tell the story of human antiquity. Beginning in the 20th century, the collection and display of human remains by museums has been met with resistance and contestation both in localized cultural and geographical contexts, and through globalized movements for repatriation and decolonization. In Canada, the strong activism of Indigenous communities to repatriate material culture and ancestral remains has made the display of any human remains an uncommon practice with a few exceptions. In the United Kingdom, archaeologists and museum curators must navigate the public’s desire to view their own prehistoric ancestors with shifting global practices. This dissertation observes how human remains came to be included within Western (Canadian and British) museum collections, how they have been displayed in museums, and how they have been contested by publics and become the centre of global movements for repatriation led by Indigenous nations around the world. Rather than take up the display of human remains as an ethical debate, it seeks to understand how bodies are otherwise materialized in museum exhibitions through ethnographic fieldwork and case studies. Drawing from new materialist and posthumanist theory, it considers how museum exhibitions “produce” bodies through a range of material and discursive practices that challenge notions of humanism and authenticity. While wider national contexts are considered, particular exhibitions are identified at the Canadian Museum of History and Canadian War Museum in Ottawa, Canada, and the National Museum of Scotland in Edinburgh, Scotland. Site visits and exhibition analysis are further considered alongside qualitative interviews with museum curators, interpretive planners, conservators and archaeologists. The dissertation finally considers the future of posthumanist approaches to studying and interpreting bodies and past human life in the museum.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.027 | 0.014 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".