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Record W2739976708 · doi:10.5539/ijel.v7n5p135

Metaphor as a Means of Pessimism in English Poetry

2017· article· en· W2739976708 on OpenAlexvenueno aff
Mohamed Ayed Ibrahim Ayassrah, Ali Odeh Hammoud Alidmat

Bibliographic record

VenueInternational Journal of English Linguistics · 2017
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsnot available
Fundersnot available
KeywordsPessimismMetaphorPoetryFeelingAestheticsLiteratureEPICAphorismSociologyHistoryPsychologyEpistemologyPhilosophyLinguisticsArt

Abstract

fetched live from OpenAlex

The present study attempts to investigate using metaphor as a powerful tool of pessimism in poetic texts with special emphasis on T.S Eliot’s Waste Land. Eliot’s Waste Land which is heavily pregnant of metaphors is a great epic poetic story summarizes the gloomy circumstances of the European life after the World War I where a complexity of sad feelings dominates the whole five parts of the poem. Eliot vividly used metaphor as an effective means in transferring the real degradation of the European life after the Great War.This study includes an introduction, significance of the study, choosing the metaphorical pessimistic expressions in Eliot’s Waste Land, questions of the study, objectives of the study, methodology, what is metaphor? functions of metaphor, what is pessimism? The Waste Land, Eliot’s life, why was Eliot pessimist in his great Waste Land? the analysis session, the answers of the study questions and the references.

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)0.001
metaresearch head score (Gemma)0.106
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.726
Threshold uncertainty score0.902

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.106
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.339
Teacher spread0.315 · 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 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
Published2017
Admission routes1
Has abstractyes

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