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Record W2268739268 · doi:10.7202/1062368ar

“You Are Turning into a Hive Mind”: Storytelling, Ecological Thought, and the Problem of Form in Generation A

2019· article· en· W2268739268 on OpenAlexaffvenue
Jenny Kerber

Bibliographic record

VenueStudies in Canadian Literature · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicUtopian, Dystopian, and Speculative Fiction
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsAgency (philosophy)Scale (ratio)StorytellingSubject (documents)EcologySociologyCognitive sciencePsychologyComputer scienceSocial scienceGeographyLiteratureArtNarrativeBiology

Abstract

fetched live from OpenAlex

This article discusses the relationship between literary form and contemporary ecological anxiety in Douglas Coupland’s novel Generation A. Coupland’s speculative fiction envisions a possible future in the wake of Colony Collapse Disorder, but the more generalized eco-anxiety the novel explores is applicable to a number of contemporary environmental issues ranging from climate change to ocean acidification. I argue that Coupland’s novel invites readers to consider the problem of representing ecological problems characterized by global scale, temporal uncertainty, and multiple origins. I then explore how Coupland responds to these challenges by stretching form in two directions. First, he juxtaposes and recycles a series of stories in a manner that capitalizes on lateral, shortened forms of attention, leading readers to detect larger patterns of significance within a database of what might initially seem like insignificant or banal details. Second, he cultivates the development of a form of “hive mind” among characters and readers that stretches ideas of personhood beyond the corporeal boundaries of the individual subject. The latter opens new possibilities for conceiving of a collective, networked mode of political agency in the era of social media and global scale effects.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.968
Threshold uncertainty score0.996

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 communication0.0000.000
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 head0.025
GPT teacher head0.249
Teacher spread0.224 · 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

Citations3
Published2019
Admission routes2
Has abstractyes

Explore more

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