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Witnessing Painful Pasts: Understanding Images of Sports at Canadian Indian Residential Schools

2019· article· en· W2972244631 on OpenAlexaffabout
Taylor McKee, Janice Forsyth

Bibliographic record

VenueJournal of Sport History · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsWestern University
Fundersnot available
KeywordsContext (archaeology)CredenceIndigenousNewspaperColonialismNarrativeResidential schoolIdeologyMedia studiesSociologyPerceptionAdvertisingGender studiesHistoryPolitical sciencePsychologyArtPoliticsLawLiteratureComputer scienceSocioeconomics

Abstract

fetched live from OpenAlex

Abstract Images are powerful tools for shaping perceptions of the past. In the context of sport at Canadian Indian residential schools, photographic images were consciously constructed and carefully selected and have been subsequently recirculated by contemporary media. Images of smiling, happy children at play at Canadian Indian residential schools have been used to lend credence to notions of sport as an unquestionable force for good without considering the context in which the images were created. In this paper, we explore how media, including online public repositories and newspapers, have taken up images of sport, specifically hockey, at Indian residential schools and how they evoke ideas about the nation and Indigenous–settler relations in Canada. We argue that photographs of residential school sports reinforce colonial narratives that lend ideological weight to settler colonial rule.

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)0.002
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 score0.138
Threshold uncertainty score0.279

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0190.020
Scholarly communication0.0100.004
Open science0.0020.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.068
GPT teacher head0.309
Teacher spread0.241 · 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

Citations6
Published2019
Admission routes2
Has abstractyes

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