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Record W4243337125 · doi:10.32920/ryerson.14647845

(Re)Writing Canadian Space: Dystopian Geographies in Larissa Lai’s Salt Fish Girl and M.G. Vassanji’s Nostalgia

2021· preprint· en· W4243337125 on OpenAlexaffabout
Hannah Warkentin

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldArts and Humanities
TopicCrime and Detective Fiction Studies
Canadian institutionsToronto Metropolitan UniversityCentre for Social InnovationYork UniversityUniversity of Calgary
Fundersnot available
KeywordsDystopiaContext (archaeology)IdeologyAestheticsPoliticsUtopiaSociologyLiteratureHistoryArtArt historyPolitical scienceLawArchaeology

Abstract

fetched live from OpenAlex

This paper examines how dystopian fiction opens up a productive space for disrupting naturalized assumptions, and shifting our understanding of taken-for-granted spaces. Drawing on Doreen Massey’s (2005) proposal that space must be seen as the product of constant interrelations, I argue that dystopian literature can similarly prompt us to reconsider our relationship to the spaces we inhabit. Using the concept of the “critical dystopia,” I examine how dystopian frameworks are operationalized in the Canadian context through a comparative analysis of two novels that speculate distinctly Canadian dystopian futures: Larissa Lai’s Salt Fish Girl (2002) and M.G. Vassanji’s Nostalgia (2016). By applying Massey’s theorization of space—its multiplicities, complexities, and political potentialities—to an examination of how Canadian spaces are transformed in the dystopian context, I then analyze how those representations challenge the spatial ideologies associated with globalization, and resist the neoliberal view of space as a surface to be crossed and conquered (Massey, 2005).

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.430
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.032
GPT teacher head0.243
Teacher spread0.210 · 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
Published2021
Admission routes2
Has abstractyes

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