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Record W3129121243

서사와 재현의 정치학: 토마스 킹의 원주민 서사의 로컬 서사로서의 가능성에 대해서

2016· article· ko· W3129121243 on OpenAlexaboutno aff
이유혁

Bibliographic record

Venue영어영문학연구 · 2016
Typearticle
Languageko
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousNarrativePoliticsIdeologyState (computer science)Representation (politics)Identity (music)SociologyGender studiesPolitical scienceAestheticsLawLiteratureArt
DOInot available

Abstract

fetched live from OpenAlex

This article examines the politics of representation in Thomas King`s indigenous narratives by analyzing his short stories in One Good Story, That One and his essays in The Truth about Stories: A Native Narrative. In his writings, not only does King criticize the process of building a modern nation-state in Canada, particularly the ways in which colonizing governing ideology becomes dominant in the process, but he also attempts to construct narratives that can contribute to (re)conceptualizing indigenous peoples` identity. This article is particularly concerned with the ways in which King`s representation of indigenous narratives serves as local narratives that can run counter to a colonizing central or national narrative. This study hopes to show how King seeks to articulate resistant local voices by indigenous peoples and contributes to (re)gaining the spatio-cultural and political position of indigenous peoples` narratives in Canada. Furthermore, it will assist us to (re)discover and (re)consider a cultural and political topography of indigenous peoples in Canada, in the situation that voices of indigenous peoples have long been marginalized, distorted, and erased.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.111
Threshold uncertainty score0.250

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 studies0.0200.016
Scholarly communication0.0070.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.220
Teacher spread0.209 · 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 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
Published2016
Admission routes1
Has abstractyes

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