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Record W3108773324 · doi:10.1177/0021989420969768

Ghosting history/historicizing the ghost: Time passage in T. C. Haliburton’s <i>The Old Judge</i>

2020· article· en· W3108773324 on OpenAlexaboutno aff
Agnieszka Kliś-Brodowska

Bibliographic record

VenueThe Journal of Commonwealth Literature · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicShort Stories in Global Literature
Canadian institutionsnot available
Fundersnot available
KeywordsHistoryPoliticsContext (archaeology)Identity (music)Optimal distinctiveness theoryRepresentation (politics)ColonialismNova scotiaEmpireGenealogyLiteratureEthnologySociologyArtAestheticsLawAncient historyArchaeologyPolitical science

Abstract

fetched live from OpenAlex

This article investigates the representation of time in T. C. Haliburton’s The Old Judge as shaped by the writer’s British North American context as well as his political background and agenda. It pays attention to the manner in which the text prepares the ground for native identity formation by providing a version of Nova Scotia’s recent history that is nonetheless presented as bygone and ancient. In The Old Judge, temporal distance of the past, apart from its richness — both confirmed by the presence of the properly historicized settler ghost — is the condition for cultural distinctiveness, maturity and heritage. Approaching The Old Judge from the perspective of Cynthia Sugars’ Canadian Gothic and Lorenzo Veracini’s settler colonialism, I argue that the text represents the past of the province as curiously extended in time in the Old World fashion, so that it may encompass the stages of cultural development required to gain the Empire’s recognition. Simultaneously, the text’s intricate play with heterochronies suggests that Haliburton’s Nova Scotia contains heterotopic spaces in Foucauldian terms, where the ordinary time passage is necessarily breached for the colony to attain proper legacy and distinct cultural status.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.878
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.003
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 head0.024
GPT teacher head0.211
Teacher spread0.187 · 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
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
Published2020
Admission routes1
Has abstractyes

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