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Record W3038521463 · doi:10.71781/14874

A Hauntology of Sheila Watson's The Double Hook

2019· dissertation· en· W3038521463 on OpenAlexaboutno aff
William Brubacher

Bibliographic record

VenueOpen MIND · 2019
Typedissertation
Languageen
FieldArts and Humanities
TopicContemporary Literature and Criticism
Canadian institutionsnot available
Fundersnot available
KeywordsWatsonHookArt historyArtLiteraturePhilosophyComputer scienceEngineering

Abstract

fetched live from OpenAlex

Ce mémoire est une lecture hantologique du roman The Double Hook de Sheila Watson. Une telle lecture accorde une importance particulière aux fantômes et aux spectres qui se trouvent dans un texte ou qui le hantent. La hantologie étant un mouvement de pensée introduit par Jacques Derrida dans Spectres de Marx, cet ouvrage de Derrida se veut à la fois un point de départ et un site important de mon analyse auquel je retourne tout au long de ce mémoire. De plus, à travers les écrits de plusieurs spécialistes de la littérature canadienne-anglaise tels que Marlene Goldman, Margaret Turner et Cynthia Sugars, ce mémoire explore ce que le roman de Watson permet de découvrir à propos de ce qui hante l’imaginaire collectif canadien. Dans une première partie de ce mémoire, je concentre mon analyse sur les spectres textuels qui hantent les pages du roman de Watson. Les mythes autochtones, les récits chrétiens, les conventions du ‘Western’ et du roman régional, ainsi que les traces de plusieurs textes modernistes, semblent hanter la structure du roman et l’utilisation du langage qui crée l’histoire présentée par Watson. Dans le deuxième chapitre de ce mémoire, mon analyse se tourne vers les fantômes et les personnages fantomatiques qui existent dans le monde fictionnel créé par Watson. Les personnages tels que la mère de la famille Potter et Coyote sont fréquemment associés aux tropes du gothique et lus comme étant des spectres et ce sont de telles lectures qui ponctuent mon analyse de cet important roman.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.927
Threshold uncertainty score0.988

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0130.000

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 head0.052
GPT teacher head0.300
Teacher spread0.249 · 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 designNot applicable
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

Citations0
Published2019
Admission routes1
Has abstractyes

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