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Record W4226515753 · doi:10.33137/ijournal.v7i1.37898

A case study of the Shingwauk Residential Schools Centre: A Survivor-centred toolkit for reconciliation

2021· article· en· W4226515753 on OpenAlexaffvenueabout
Jillie Reimer

Bibliographic record

VenueThe iJournal Student Journal of the Faculty of Information · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicOral History, Memory, Narrative Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsExhibitionAutonomyIndigenousParticipatory action researchSociologyCitizen journalismWork (physics)Public relationsPedagogyPolitical scienceVisual artsEngineering

Abstract

fetched live from OpenAlex

This case study analyzes the programming initiatives of the Shingwauk Residential Schools Centre (SRSC) with the aim to produce an actionable toolkit of six distinct principles for institutions in addressing residential school history and working with Survivors. These six principles—applying Survivor-led, community-first methods; engaging in co-creative and participatory work; re-contextualizing photography through oral history; reclaiming Indigenous objects, spaces, and time; continually re-examining prior reconciliation efforts; and supporting the autonomy of witnessing—all contribute to the ongoing Canadian response to the Truth and Reconciliation Commission of Canada's Calls to Action in confronting systemic racism towards Indigenous peoples, educating the public about residential schools, and advancing the progress of reconciliation efforts. The SRSC’s programming, including the Remember the Children project and the Reclaiming Shingwauk Hall exhibition, along with the centre’s further statements and initiatives, all highlight best practices in the preservation and programming for former residential school sites. By following the methods realized throughout this case study, other institutions can mindfully and meaningfully co-create Survivor-led spaces and programming that will further reconciliation’s aims—listening, learning, supporting, healing, and restoring.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.022
Threshold uncertainty score0.721

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.059
GPT teacher head0.282
Teacher spread0.224 · 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 teacher head, 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

Citations0
Published2021
Admission routes3
Has abstractyes

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