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Record W4318214930 · doi:10.4000/books.pulm.11048

The Politics and Poetics of Thomas King’s Textual Hauntings

2009· book-chapter· en· W4318214930 on OpenAlexaboutno aff

Bibliographic record

VenuePresses universitaires de la Méditerranée eBooks · 2009
Typebook-chapter
Languageen
FieldArts and Humanities
TopicAmerican and British Literature Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPoeticsUncannyPoliticsLiteratureMotif (music)ArtPhilosophyPoetryPsychoanalytic theoryNarrativityStatement (logic)Art historyNarrativePsychoanalysisEpistemologyAestheticsLaw

Abstract

fetched live from OpenAlex

This paper analyzes the use of the Indian ghost motif in Thomas King’s writings within the framework of postcolonial criticism about spectral narrativity, and in the light of both Freud’s psychoanalytic theory of the uncanny and Derrida’s notion of spectrality. King outlines a politics and poetics of haunting that wholly contradicts Canada’s alleged ghostlessness, as expressed not only by pioneer Catharine Parr Traill in her famous 1833 statement about the complete banishment of ghosts and spirits from a ‘too matter-of-fact country for such supernaturals to visit’, but also by Earle Birney, who concluded his poem ‘Can. Lit.’ asserting: ‘it’s only by our lack of ghosts/we’re haunted’.Unlike other instances of the genre, King’s textual hauntings are never horrific because the presence of spectral Native figures in his novels and short stories does not include any malevolent or macabre elements. Yet the subversive humor which often pervades these mysterious appearances and other fantastic manifestations in King’s poignant scenes of magic realism becomes an amazingly effective strategy to revise colonial history and raise important issues concerning the present day lives of Natives in North America, such as stereotyping, cultural appropriation and commodification, sovereignty, environmental degradation and territorial claims.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.921
Threshold uncertainty score1.000

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 studies0.0010.002
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 head0.009
GPT teacher head0.194
Teacher spread0.185 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations2
Published2009
Admission routes1
Has abstractyes

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