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Record W4312598751 · doi:10.2478/abcsj-2022-0004

Nighttime Invasions, Colonial Dispossession, and Indigenous Resilience in Richard Wagamese’s <i>Indian Horse</i>

2022· article· en· W4312598751 on OpenAlexaboutno aff
Doro Wiese

Bibliographic record

VenueAmerican, British and Canadian Studies · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
FundersH2020 Marie Skłodowska-Curie ActionsUniversity of Warwick
KeywordsIndigenousColonialismCapitalismNarrativeStorytellingModernityGender studiesSociologyPoliticsHistoryAestheticsPolitical scienceLiteratureArtLawEcology

Abstract

fetched live from OpenAlex

Abstract This essay demonstrates how Richard Wagamese employs oral storytelling techniques to make the complex idea of Indigenous dispossession sensually and intellectually accessible to readers of his novel, Indian Horse . The Wabseemoong First Nation writer depicts the devastating effects of white entitlement when rendering character Saul Indian Horse’s experiences in Canada’s residential schools and the effect those schools had on his subsequent life. Using narrative analysis, it will be shown how Saul loses his ability to perceive places as being alive and resonant, and is thereby dispossessed on an individual, social, and spiritual level. Furthermore, Wagamese’s descriptions of the physical, emotional, and sexual abuse of Indigenous children draw attention to the harrowing histories of Canada’s residential schools, thus laying bare the necropolitical potential of settler-colonial dispossession. This essay links Wagamese’s narrative to recent arguments brought forward in Indigenous studies. It aims to demonstrate that Indigenous dispossession in settler colonial states is part of modernity, and its overarching political economy, that is capitalism. i

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.627
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0130.001
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 head0.011
GPT teacher head0.276
Teacher spread0.264 · 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 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 routes1
Has abstractyes

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