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Record W2995687204 · doi:10.15366/actionova2019.3.009

Richard Wagemese's Indian Horse: Stolen Memories and Recovered Histories

2019· article· es· W2995687204 on OpenAlexaboutno aff
Franck Miroux

Bibliographic record

VenueACTIO NOVA Revista de Teoría de la Literatura y Literatura Comparada · 2019
Typearticle
Languagees
FieldArts and Humanities
TopicCultural and Social Studies in Latin America
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesNarrativeEthnologyArtHistoryLiterature

Abstract

fetched live from OpenAlex

Este artículo pone de manifiesto las estrategias narrativas mediante las cuales el autor anishinaabe canadiense Richard Wagamese somete al lector de su novela Indian Horse (2012) a la misma violencia sufrida por el joven héroe cuando la repentina resurgencia del recuerdo traumático reprimido acaba rompiendo la linealidad aparente de su historia. Asimismo, este estudio pretende demostrar que la reapropiación de la memoria robada, y por la tanto la posibilidad de reconstruirse tras el traumatismo vivido, pasan por una reapropiación de las formas aborígenes del relato gracias a las que Wagamese contribuye de manera significativa a la reescritura de la historia de las escuelas residenciales autóctonas de Canadá. This paper purports to explore the narrative devices which enable the Anishinaabe Canadian author Richard Wagamese to compel the reader of his novel Indian Horse (2012) to experience the same violence as that faced by the young protagonist when the repressed memory of the terrible abuse suffered at an Indian residential school resurfaces decades after, disrupting the apparently linear course of the story. This study also seeks to show that Wagamese offers a major contribution to the rewriting of the history of residential schools in Canada by reclaiming Aboriginal narrative forms as a means to recover stolen memories, and thus to reconstruct both the fragmented (his)story and the shattered self.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.899
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0170.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.258
Teacher spread0.245 · 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.

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

Citations3
Published2019
Admission routes1
Has abstractyes

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