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Storied Geographies: Settler Extractivism and Sites of Indigenous Resurgence in Cherie Dimaline’s Empire of Wild

2022· article· en· W4312109222 on OpenAlexaboutno aff
Julia Siepak

Bibliographic record

VenueAtlantis Journal of the Spanish Association for Anglo-American Studies · 2022
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousColonialismPoeticsEmpireCONTESTStorytellingNarrativeSociologyEcocriticismHistoryEthnologyAestheticsGender studiesAnthropologyPolitical scienceLiteratureArtPoetryArchaeologyEcologyLaw

Abstract

fetched live from OpenAlex

This article offers a reading of Cherie Dimaline’s Empire of Wild (2019) that focuses on the novel’s poetics of space, which contests settler colonial extractive geographies. Adopting a strong Métis- and women’s perspective, Dimaline’s narrative explores the contemporary Métis condition, which is marked by dispossession and displacement under settler colonialism, and the precarity connected with rampant resource extraction in Canada. In order to tackle the tensions between settler- and Indigenous conceptualizations of space, I provide a brief overview of settler Canadian land politics, and describe the nation’s reliance on fossil fuels applying the concepts of petrostate and petroculture. By incorporating a Rogarou figure, a lupine monster in Métis stories, Dimaline embeds her novel within the traditional stories of her people, demonstrating their potential to critique and contest settler colonial geographies marked by extraction. The analysis approaches Indigenous storytelling as a strategy that resists dispossession and tackles the representation of Métis bodies as sites of resurgence.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.441
Threshold uncertainty score0.888

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0280.029
Scholarly communication0.0050.002
Open science0.0010.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.038
GPT teacher head0.366
Teacher spread0.328 · 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 routes1
Has abstractyes

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