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Record W2989951217 · doi:10.1093/ijtj/ijz027

Archives, Museums and Sacred Storage: Dealing with the Afterlife of the Truth and Reconciliation Commission of Canada

2019· article· en· W2989951217 on OpenAlexaffabout
Cynthia E. Milton, Anne-Marie Reynaud

Bibliographic record

VenueInternational Journal of Transitional Justice · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicMuseums and Cultural Heritage
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsCommissionAfterlifeIndigenousCollective memoryFeelingSociologyVisual artsAestheticsLawPolitical scienceArtPsychologyLiteratureSocial psychology

Abstract

fetched live from OpenAlex

Abstract∞ The Canadian Truth and Reconciliation Commission (TRC) acquired over 1,200 material submissions through the gifts it received at its events. Though other TRCs mention objects in their records, the gift-giving practice that became central to TRC events in Canada was unprecedented, and so is its large collection of TRC-gifted objects today. The Canadian TRC is thus unique and faces the challenges of categorizing, preserving, displaying and honouring these material artefacts. What has been the post-TRC life of these objects and art pieces? What is their role in creating a collective memory of residential schools and how might they promote reconciliation? This article shows that the post-TRC life of the objects opens up new museological spaces and practices through the ways the objects are curated (or not) for remembering and learning about residential schools according to Indigenous protocols and ways of thinking and feeling.

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)0.007
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: none
Teacher disagreement score0.859
Threshold uncertainty score0.997

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0630.028
Scholarly communication0.0230.005
Open science0.0020.010
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0110.000

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 head0.007
GPT teacher head0.175
Teacher spread0.168 · 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

Citations8
Published2019
Admission routes2
Has abstractyes

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