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Record W2772268067 · doi:10.3808/jei.201700374

Environmental Information in Modern Fiction and Ecocriticism

2017· article· en· W2772268067 on OpenAlexaff
J. Zhang, Lirong Liu, Xiao-Hua SHI, Hao Wang, Guohe Huang

Bibliographic record

VenueJournal of Environmental Informatics · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicWater-Energy-Food Nexus Studies
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsEcocriticismAnthropocentrismApocalypticismEnvironmental ethicsNatural (archaeology)SituationismSophisticationEnvironmentalismSociologySocial scienceHistoryAestheticsPolitical sciencePhilosophyLawArchaeologyPolitics

Abstract

fetched live from OpenAlex

Environmental writing and ecocritical inquiry have been practiced more vigorously in recent years than before, with increasing sophistication and substantial progress. In this study, the discourses of environment in modern fiction are examined from an ecocritical perspective. The literary representations of environment in modern fiction reveal new insights into environmental issues and provide new perspectives and viable documentary information for the scientific study of the environment. Trying to conceptualize some of the environmental phenomena, this study concludes that zoomorphism and anti-anthropocentrism can well balance ecocentric concerns, reflecting and enhancing the close ties and interdependence between human society and the natural world. Environmental apocalypticism is another notable concept conveyed in modern fiction, indicating great crises of the worsening environment. More importantly, environmental apocalypticism serves as an alarming reminder that the remarkable complexity of problematic environmental issues humans facing are both urgent and devastating. With analyses of literature’s engagement with the natural environment, the interdisciplinary vision highlights the interconnections between man and nature, expands research space for both disciplines and provides more efficient means of solving the environmental problems.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.554
Threshold uncertainty score0.668

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 communication0.0000.005
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 head0.008
GPT teacher head0.199
Teacher spread0.191 · 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 designObservational
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
Published2017
Admission routes1
Has abstractyes

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