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Privatized Futures, Climate Control, and Resistance in Recent Scottish Dystopian Fiction

2022· article· en· W4312170102 on OpenAlexaff
Peter Clandfield

Bibliographic record

VenueSillages critiques · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicContemporary Literature and Criticism
Canadian institutionsMacEwan University
Fundersnot available
KeywordsDystopiaFutures contractState (computer science)Resistance (ecology)HistoryBrotherAestheticsSociologyLawPolitical scienceArtComputer science

Abstract

fetched live from OpenAlex

This article addresses Scottish dystopian novels that move past ideas of the British state as Big Brother to envision future Scotlands encountering global problems of climate change and its exploitation by neoliberal regimes. After discussing Alasdair Gray’s 1982, Janine (1984) as an influential confrontation with the increasingly toxic military-industrial state of 1980s Britain, the essay interprets Matthew Fitt’s But n Ben A-Go-Go (2000), John Aberdein’s Strip the Willow (2009), and the multiple-author graphic novel IDP: 2043 (2014), edited by Denise Mina, as what Umberto Eco calls “novels of anticipation,” or warnings of the undesirable eventualities that present tendencies may bring about. The essay shows how these novels also anticipate recent critical perspectives on climate change and dystopia, particularly Amitav Ghosh’s call (2016) for fiction that confronts the potentially intractable effects of global weather events, and Tom Moylan’s advocacy (2020) of works that resist presenting dystopian spectacles for passive consumption and instead call upon readers for active, constructive interpretation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.024
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0160.062
Scholarly communication0.0090.005
Open science0.0010.006
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0050.000

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 head0.012
GPT teacher head0.231
Teacher spread0.218 · 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 source (direct Gemma or distilled Codex), 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
Published2022
Admission routes1
Has abstractyes

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