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Record W4313030496 · doi:10.1051/e3sconf/202235903002

“Dirty Energy” and Ecological Performativity in Contemporary English Poems: Critiquing Petro Culture of the Anthropocene

2022· article· en· W4313030496 on OpenAlexaboutno aff
Henrikus Joko Yulianto, Zuhrul Anam

Bibliographic record

VenueE3S Web of Conferences · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicEcocriticism and Environmental Literature
Canadian institutionsnot available
Fundersnot available
KeywordsMateriality (auditing)AnthropocenePoetryPerformative utteranceSociologyAestheticsEnvironmental ethicsLiteratureArtPhilosophy

Abstract

fetched live from OpenAlex

Fossil fuels will always take command of human’s daily life. Despite being “dirty energy”, humans cannot jettison them since they are the mainstay for multipurpose energies. They are more dependable and accessible than renewable energy sources such as hydropower, solar panel, and wind power. Even more so, in this present globalization the increasing scale of consumerism via digital technology and social media consumes the fuels. This petro-overconsumption of the fuels and their derivative products such as plastic certainly has some detrimental impacts: the more emission of carbon dioxide and other toxic particles to the atmosphere. Contemporary English poems are some works that critique the petro-overconsumption. A Canadian poet, Stephen Collis in his poem “Take Oil & Hum”; a Hawaiian poet, Craig Santos Peres in his poem about plastic, “The Age of Plastic”; and two Indonesian poets, AfrizalMalna in “petrol cupboard” and F. Aziz Manna in “Estuary” are the epitome of ecopoems that share this concern. With their performative interiorizing of petro-materiality, their depiction of petro-transcorporeality from one form into another, they articulate the polemics and impacts of non-renewable energy on human and nonhuman creatures as the issue of ecological precarity in the era of anthropocene.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.722
Threshold uncertainty score0.999

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)0.0020.000

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 head0.029
GPT teacher head0.222
Teacher spread0.192 · 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.

Study designTheoretical or conceptual
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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