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Record W4406699682 · doi:10.14201/candb.v14i27-43

Re-Creation, Re-Membrance, and Resurgence: Richard Wagamese’s Indian Horse

2025· article· en· W4406699682 on OpenAlexfundaboutno aff
Celia Cores Antepazo

Bibliographic record

VenueCanada and Beyond A Journal of Canadian Literary and Cultural Studies · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicAustralian History and Society
Canadian institutionsnot available
FundersUniversidad de SalamancaUniversity of TorontoInternational Council for Canadian StudiesGovernment of Canada
KeywordsHorseEconomic historyHistoryBiology

Abstract

fetched live from OpenAlex

This article examines the novel Indian Horse (2012), written by Ojibwe Wabaseemoong Independent Nations member Richard Wagamese (1955-2017) at the height of the Truth and Reconciliation Commission era. Wagamese finds inspiration in the testimonies and experiences of hundreds of victims of Canada’s residential school system, including those of his own family members. The article contextualizes the novel in the Truth and Reconciliation Commission era and explores Saul’s narrative journey to recover his suppressed memories of personal and collective abuse at St. Jerome’s Indian Residential School through the lens of Indigenous resurgence and grounded normativity. Thus, the paper draws on Michi Saagiig scholar Leanne Betasamosake Simpson’s writings on Indigenous radical resurgence to explore the retrieval of Indigenous ways of existing in the world as the way towards decolonization and Indigenous sovereignty. The paper argues that Saul is able to overcome his trauma-induced amnesia, born from the necessity to endure and adapt, and to escape the spiral of shame, isolation, and self-destruction in which he engages only after he embraces discursive Indigenous ways of healing. Wagamese therefore constructs a narrative in which the protagonist’s development mirrors the ideal that the author sets for Canada, in which reconciliation with Indigenous truth will not take place unless the whole story is acknowledged.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.457
Threshold uncertainty score0.966

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.018
GPT teacher head0.280
Teacher spread0.262 · 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 designNot applicable
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
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueCanada and Beyond A Journal of Canadian Literary and Cultural StudiesSame topicAustralian History and SocietyFrench-language works237,207