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Record W2063726932 · doi:10.3138/utq.82.2.278

Settler Nationalism and the Foreign: The Representation of the Exogene in Ernest Thompson Seton’s <i>Two Little Savages</i> and Dionne Brand’s <i>What We All Long For</i>

2013· article· en· W2063726932 on OpenAlexvenueaboutno aff
Lee Frew

Bibliographic record

VenueUniversity of Toronto Quarterly · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsIdentity (music)NationalismRepresentation (politics)IndigenizationColonialismHistoryNational identityCanadian literatureArticulation (sociology)SociologyGender studiesLiteratureLawAestheticsPolitical scienceArtAnthropology

Abstract

fetched live from OpenAlex

abstract The privilege afforded to anglophone whiteness in English Canada would seem to be at odds with the cliché that Canadian national identity is elusive. As a settler society, English Canada imagines itself relative to the others it defines within a complex process of indigenization. Critical attention has usually focused on the way the indigenous is represented to insist upon the seamless integration of settler cultures into their physical environments and to exonerate them from traumatic colonial histories. Viewing indigenization as a simultaneous abnegation of foreign traits, this essay investigates the signifying process as it relates to ‘exogeneity’, or foreign otherness, in English Canada. A comparative analysis of Ernest Thompson Seton’s Two Little Savages (1903) and Dionne Brand’s What We All Long For (2005) reveals the way in which consistent representations of the foreign – the ‘exogene’ and the ‘exogenous’ – remain integral to the articulation of national identity in English Canada.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.393
Threshold uncertainty score0.356

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 studies0.0000.001
Scholarly communication0.0000.001
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 head0.009
GPT teacher head0.214
Teacher spread0.204 · 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 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
Published2013
Admission routes2
Has abstractyes

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