Approaches to establishing meaningful partnerships with Indigenous groups: an examination of protocol from eight museums
Bibliographic record
Abstract
Museums are often regarded as respected places of learning; however, they have played a significant role creating and disseminating stereotypes about Indigenous People by misrepresenting them and their cultures. This, coupled with the often violent way that material culture has been collected, has left museums with legacies that can be harmful and unwelcoming to Indigenous Peoples. Indigenous activism both within and outside heritage spheres has led to documents such as UNDRIP, which affirm Indigenous sovereignties and rights to their culture and heritage, and have set a new precedent for how museums should operate and represent Indigenous cultures. This thesis examines eight museums from across Canada, the USA, and Germany to understand how they are engaging with the Indigenous Nations they represent, and further, how they are counteracting their legacies. This is coupled with the observation of the first stage of Creating Context, a community-project that brought Nunatsiavummiut to Germany to reconnect with material culture in two museum’s care. It was found that the establishment of meaningful relationships is based in trust, and brought to action with three guiding principles (1) ontological empathy; (2) power-shifting and (3) culturally specific care protocol. These themes are foundational in guiding museums toward a better museum practice.
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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 | 0.150 | 0.210 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.030 | 0.017 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".