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Record W3160298846 · doi:10.1386/ajpc_00031_1

Survival is insufficient: Degenerate utopian nostalgia in popular culture post-apocalyptic fiction

2020· article· en· W3160298846 on OpenAlexaff
Kirsten Bussière

Bibliographic record

VenueAustralasian Journal of Popular Culture · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicGothic Literature and Media Analysis
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsUtopiaIdeologyMythologyPopular cultureNarrativeState (computer science)AestheticsHistoryEnvironmental ethicsSociologyLawPhilosophyMedia studiesPolitical scienceLiteratureArt historyArtClassics

Abstract

fetched live from OpenAlex

From SARS to H1N1, and most recently COVID-19, global disease outbreaks have defined the past several decades. For many, we are living in what can only be described as a pre-apocalyptic moment. Indeed, we are currently facing a global pandemic outbreak – a situation that had been previously described as imminent and perhaps even long overdue. Consequently, the publication of pandemic narratives has increased exponentially, which exposes a heightened social concern about the risk of viral outbreak. But instead of speaking to these growing anxieties and providing models to interpret our current position, a growing body of popular culture post-apocalyptic fiction remains deeply entrenched in a dangerous nostalgia that undermines the construction of hypothetical models that could appropriately respond to these threats. I argue that these texts can therefore be read as degenerate utopias , Louis Marin’s term for the false utopian myths that circulate within a society. A degenerate utopia is thus not really a utopia at all, but rather an ideology that elevates the past to a false state of perfection. My article examines the construction of degenerate utopian realities through collective memory in Emily St John Mandel’s Station Eleven and Peter Heller’s The Dog Stars .

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.843
Threshold uncertainty score0.909

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.285
Teacher spread0.263 · 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 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

Citations5
Published2020
Admission routes1
Has abstractyes

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