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Record W6947809334 · doi:10.48336/1w7m-nw70

Approaches to establishing meaningful partnerships with Indigenous groups: an examination of protocol from eight museums

2025· article· en· W6947809334 on OpenAlexaffabout

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2025
Typearticle
Languageen
FieldComputer Science
TopicResearch Data Management Practices
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsIndigenousCultural heritageAction (physics)Traditional knowledgeIndigenous cultureSet (abstract data type)Museology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.903
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0010.000
Scholarly communication0.0020.025
Open science0.0070.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.181
GPT teacher head0.310
Teacher spread0.130 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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