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Risk, Mortality, and Memory: The Global Imaginaries of Cherie Dimaline’s The Marrow Thieves, M.G. Vassanji’s Nostalgia, and André Alexis’s Fifteen Dogs

2019· article· en· W2946143078 on OpenAlexafffundabout
Diana Brydon

Bibliographic record

VenueRevista Canaria de Estudios Ingleses · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicEcocriticism and Environmental Literature
Canadian institutionsUniversity of Manitoba
FundersCanada Research Chairs
KeywordsContext (archaeology)HumanismAgency (philosophy)SociologySubjectivityFableEnvironmental ethicsAestheticsHistorySocial sciencePolitical scienceLiteratureLawPhilosophyArtEpistemology

Abstract

fetched live from OpenAlex

Este artículo examina tres novelas canadienses contemporáneas basadas en la sociedad del\n\t\t\t\t riesgo global usando un modo ficticio especulativo que inquiere: «¿Qué pasaría si?» The\n\t\t\t\t Marrow Thieves (2017), de Cherie Dimaline, y Nostalgia (2016), de M.G. Vassanji, imaginan\n\t\t\t\t sociedades distópicas devastadas por el cambio climático para analizar los ideales humanistas\n\t\t\t\t del Progreso. Fifteen Dogs (2015), de André Alexis, usa la fábula animal para abordar qué\n\t\t\t\t significa ser un animal mortal. Cada una profundiza en las implicaciones de una conciencia\n\t\t\t\t de riesgo para la agentividad y la ética: para los pueblos indígenas en The Marrow Thieves;\n\t\t\t\t para los habitantes de Toronto en el contexto de un apartheid global intensificado en Nostalgia;\n\t\t\t\t y para los perros que luchan con una inteligencia humana otorgada por Dios en el\n\t\t\t\t Toronto contemporáneo de Fifteen Dogs. Al negociar el riesgo, cada ficción recurre al papel\n\t\t\t\t de la memoria, la creatividad y las formas alternativas de subjetividad y comunidad para\n\t\t\t\t garantizar la supervivencia. Las tres narrativas dan pasos sutiles, aunque necesarios, hacia\n\t\t\t\t futuros alternativos con la habilidad de imaginar de una forma distinta.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.392
Threshold uncertainty score0.615

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 communication0.0000.000
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 head0.016
GPT teacher head0.230
Teacher spread0.214 · 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 designObservational
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
Published2019
Admission routes3
Has abstractyes

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