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Record W2959920877 · doi:10.29311/mas.v17i2.2806

Museums, Decolonization and Indigenous Artists as First Cultural Responders at the new Canadian Museum for Human Rights

2019· article· en· W2959920877 on OpenAlexaboutno aff
Stephanie Anderson

Bibliographic record

VenueMuseum and Society · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicMuseums and Cultural Heritage
Canadian institutionsnot available
Fundersnot available
KeywordsExhibitionIndigenousDecolonizationColonialismHuman rightsPretextNarrativeSociologyIdentity (music)ConsciousnessMedia studiesEconomic JusticeLawAestheticsPolitical scienceVisual artsArtLiteraturePsychology

Abstract

fetched live from OpenAlex

The Canadian Museum for Human Rights (CMHR) is part of a global movement of human-rights–driven museums that commemorate atrocity-related events through exhibitions aimed to communicate a national social consciousness. However, museums in Canada are increasingly understood to contribute to the perpetuation of settler colonial memory regimes as dominant narratives of national identity. Through the analysis of theexhibit ‘Aborigina lWomen and the Right to Safety and Justice’, this article explores how museums in represent difficult knowledge and act as sites of decolonization, while suggesting how shared authority and nuanced Indigenous art forms might play a role in both. It posits that if museums in settler colonial societies are to evolve beyond the pretext of detached host, they must not only acknowledge past atrocities and injustices against Indigenous peoples, but also consistently examine the colonial logics and inventions that permeate colonizing and decolonizing exhibitions.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.302

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0380.031
Scholarly communication0.0110.003
Open science0.0020.009
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0100.000

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 head0.015
GPT teacher head0.226
Teacher spread0.211 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations5
Published2019
Admission routes1
Has abstractyes

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