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Record W4281615723 · doi:10.32316/hse-rhe.v34i1.4953

“Were It Not for the Spirit of the Boys… There Would Have Been No Story”: Memory and Childhood in Residential School Narratives

2022· article· en· W4281615723 on OpenAlexafffundvenueabout
Eric Farr

Bibliographic record

VenueHistorical Studies in Education / Revue d histoire de l éducation · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of Toronto
FundersQueen's UniversityWilfrid Laurier UniversityMcGill UniversityUniversity of TorontoStrongState University of New YorkUniversity of OxfordHarvard University
KeywordsNarrativeSituatedAgency (philosophy)IndigenousContext (archaeology)SociologyGender studiesSet (abstract data type)Developmental psychologyPsychologyHistorySocial scienceArchaeologyLiterature

Abstract

fetched live from OpenAlex

This article examines how the policies and practices of the residential school system refracted the conceptual dynamics of childhood in twentieth-century Canada and shaped the lives of Indigenous children in that system. In particular, I discuss how the racializing logic of the residential system totalized or disrupted broader conceptual shifts in the relationship of childhood to the public domain and to adulthood. In this context, I draw on three residential school narratives to argue that memory played an essential role in the lives of the Indigenous students as a crucial site of creative agency, and in the residential school system’s strategy of assimilation. These narratives make a certain twentieth-century Indigenous child knowable to history, one that relies on a set of relationships, held together by memory, among the child, the adult, and the familial and communal narratives in which they are situated.

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)0.004
metaresearch head score (Gemma)0.006
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 score0.130
Threshold uncertainty score0.259

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0180.040
Scholarly communication0.0070.007
Open science0.0020.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.052
GPT teacher head0.339
Teacher spread0.287 · 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

Citations1
Published2022
Admission routes4
Has abstractyes

Explore more

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