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Record W4387578987 · doi:10.1080/00111619.2023.2268519

Climate Crises, Ruined Islands, and British Metamodernism

2023· article· en· W4387578987 on OpenAlexafffund
Emily Arvay

Bibliographic record

VenueCritique Studies in Contemporary Fiction · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicEcocriticism and Environmental Literature
Canadian institutionsUniversity of Victoria
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsGeopoliticsGratitudeObsolescenceHistoryPrecarityFutures contractClimate changeSociologyPolitical sciencePoliticsLawGender studiesPsychologyGeology

Abstract

fetched live from OpenAlex

This article contends that popular acceptance of Anthropogenic climate change in early 2000s Britain coincided with cultural efforts to redefine the historical present via transhistorical phenomena through the concretization of deep time. This article therefore situates itself in the historical juncture between the IPCC’s first report (1990) and its fourth (2007) to argue that the climatological, financial and geopolitical crises that coalesced in the 1990s prompted a shift that changed the tenor of British climate fictions published in the 2000s. To address the supranational reach of the climate crisis, British authors used metamodernist means to map the historical ruination of remote islands onto speculative futures extrapolated from the climate reports of the IPCC – thereby conjuring the climatological transformation of Earth into an Earth-like planet and the propulsion of humans toward future obsolescence. Ultimately, this article attends to the ecocritical significance of Mitchell’s Cloud Atlas (2004), Self’s The Book of Dave (2006) and Winterson’s The Stone Gods (2007) to suggest that these metamodernist climate fictions transpose the failures of submerged pasts onto near-futures drawn from present precarity to undermine the present as unique, the future as determined and the past as inaccessible and of little use to the present or future.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.515
Threshold uncertainty score0.656

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 communication0.0000.001
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 head0.120
GPT teacher head0.321
Teacher spread0.200 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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