Bibliographic record
Abstract
In this article, I argue that recontextualizing Indigenous cultural heritage through institutional acquisition and cataloging can also be understood as a jurisdictional strategy that upholds the supremacy of US and Canadian legal regimes over Indigenous laws. To do this, I share what I have learned from participating in a Nation-led, community-based research project with the Nuxalk First Nation Ancestral Governance Office, in what is currently British Columbia, Canada. Our work together focused on reinvigorating the Nation’s laws, teachings, and protocols through the evolution of their own database of Nuxalk objects, still held in museum collections worldwide. I discuss this project and how it illustrates the legal context inherent to understanding much Nuxalk material culture. Next, bringing together literature on organizing knowledge in museums, settler colonial theories of dispossession, and archival copyright law, I look at how accessioning Indigenous objects into settler collections in the US and Canada is enacting another legal process, “written on top of” the legal meanings objects hold for the Nuxalk Nation, and reframing them as objects the museum has legitimate control and possession over. I close by reflecting on the strategies Nuxalk people, and other Indigenous artists and scholars, are undertaking to challenge the normative power of museum authority through interventions that are grounded in Indigenous governance and sovereignty.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.031 | 0.070 |
| Scholarly communication | 0.020 | 0.015 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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".