Decolonize and Divest: The Changing Landscape of Oil-Sponsored Museums in Canada
Bibliographic record
Abstract
This dissertation investigates the relation between oil companies and museums in Canada. I situate museums in a moment of critical change, wherein Canadian museums lead global efforts to “decolonize” – or reform the ways they engage with Indigenous stakeholders and Indigenous collections – while maintaining financial partnerships with the oil industry. With activists and cultural workers increasingly calling for museums to divest from fossil fuels in Europe and the U.S., I interrogate the reality of museum practice in two oil-sponsored museums: the Glenbow Museum in Calgary and the Canadian Museum of History in Gatineau. Focusing on two museum exhibitions from different moments of Canadian history, I trace the political economic implications of industry support for cultural initiatives and highlight the granular experiences of museum professionals who work on such projects. I explore the first instance of contested oil sponsorship in a Canadian museum –The Spirit Sings: Artistic Traditions of Canada’s First Peoples (1988), sponsored by Shell – to argue that, despite community-informed reforms to collections care, exhibitions, or programming, Canada’s museum sector has neglected Indigenous groups’ early critiques of oil sponsors and their ties to land dispossession. The next case study explores the more recent Canadian History Hall (2017) – sponsored by the Canadian Association of Petroleum Producers – to illustrate the contemporary manifestations of extractive interests that emerge in the development of a large, national history exhibition. Together, the historical and contemporary data I collected through archival research, interviews, and document analysis point to the complexity of justice-oriented museology in a country deeply connected to exploitative resource extraction. I argue that while there is minimal overlap between sponsors and exhibition development, the contradictions of corporate funding highlight the ongoing hegemonic function of museums. Through reflective engagement with contributions from decolonial pedagogy and museology, I propose that undertheorized aspects of museum operations, including the behind-the-scenes practices of funding, should be similarly informed by the socially engaged frameworks currently underpinning museum work in Canada.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.030 | 0.014 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".