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Record W3206059508

Residential School Community Archives: Spaces of Trauma and Community Healing

2021· article· en· W3206059508 on OpenAlexaffabout
Krista McCracken, Skylee-Storm Hogan

Bibliographic record

VenueJournal of Critical Library and Information Studies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsAlgoma University
Fundersnot available
KeywordsIndigenousGriefGovernment (linguistics)Historical traumaColonialismSociologyPolitical sciencePublic relationsHistoryCriminologyLawMedicineNursing
DOInot available

Abstract

fetched live from OpenAlex

Colonial archives are sites of trauma, erasure, and grief for many marginalized communities. In Canada the vast majority of archives relating to Indigenous peoples are held by government, church, and non-Indigenous archives. Colonial archives have actively taken Indigenous culture and heritage away from communities and made it inaccessible to those who the records are about.  Many archives containing information relating to Residential Schools have just begun to grapple with the ethical and professional obligations that come from holding records that document colonial violence, abuse, death, and assimilationist practices. This article explores the practices of the Shingwauk Residential Schools Centre (SRSC) community archive and the ways in which the SRSC supports community healing and navigates traumatic archival records.   Since its establishment the SRSC archives has been a place of raw emotion and grief, but also a place of tremendous community strength, healing, and resilience. This article will explore the trauma associated with archives of Residential Schools and the ongoing navigation of archival spaces which embody loss and community. Pre-print first published online 09/28/2021

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)0.005
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 score0.982
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0230.026
Scholarly communication0.0140.008
Open science0.0020.013
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0140.001

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.

Opus teacher head0.039
GPT teacher head0.353
Teacher spread0.314 · 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

Citations2
Published2021
Admission routes2
Has abstractyes

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Same venueJournal of Critical Library and Information StudiesSame topicIndigenous Health, Education, and RightsFrench-language works237,207