MétaCan
Menu
Back to cohort
Record W2342457639 · doi:10.5539/ells.v6n2p91

Apocalyptic Imagery and Its Significance in James Fenimore Cooper’s The Prairie

2016· article· en· W2342457639 on OpenAlexvenueno aff
Sabri Mnassar

Bibliographic record

VenueEnglish Language and Literature Studies · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicEcocriticism and Environmental Literature
Canadian institutionsnot available
Fundersnot available
KeywordsMythologyMeaning (existential)HistoryOrder (exchange)PessimismEnvironmental ethicsArt historyPhilosophyArtTheologyClassicsEpistemology

Abstract

fetched live from OpenAlex

<p>This article analyzes the meaning and significance of the apocalyptic imagery and metaphors used in James Fenimore Cooper’s <em>The Prairie</em>. It examines the peculiar features and characteristics of the setting of Cooper’s novel and studies the ways and techniques through which the prairie is portrayed as an apocalyptic place. Among these techniques is Cooper’s use of ocean imagery, the gothic and apocalyptic scenes. This article also examines the importance of the setting of <em>The Prairie</em> in underlining Cooper’s own attitude towards the causes and origins of the apocalypse. It suggests that the apocalypse is caused by mankind rather than by divine will and that human environmentally-hazardous practices and behaviour are the main causes of the earth’s becoming a gloomy and uninhabitable place. In order to emphasize this idea, this paper analyzes two of the most powerful and apocalyptic scenes in Cooper’s novel which are the buffalo stampede and the prairie fire. It suggests that these scenes highlight Cooper’s warning about the threats and dangers of the thoughtless destruction of the natural environment. Due to the recurrent use of apocalyptic terms, imagery and metaphors, this article suggests that <em>The Prairie</em> is the most pessimistic of Cooper’s novels and that it announces the death of the author’s ideal myths.</p>

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.558
Threshold uncertainty score0.375

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 head0.011
GPT teacher head0.224
Teacher spread0.213 · 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 designQualitative
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
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueEnglish Language and Literature StudiesSame topicEcocriticism and Environmental LiteratureFrench-language works237,207