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Record W3015746344 · doi:10.5539/ells.v10n2p1

Conceptual Metaphors of Time in the Sonnets of Shakespeare: A Cognitive Linguistic Approach

2020· article· en· W3015746344 on OpenAlexvenueno aff
Mufeed Al-Abdullah

Bibliographic record

VenueEnglish Language and Literature Studies · 2020
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsnot available
Fundersnot available
KeywordsSynecdocheMetonymyMetaphorVariety (cybernetics)Theme (computing)SonnetConceptual metaphorCognitive semanticsCreativityLinguisticsEpistemologySociologyCognitionPsychologyPoetryPhilosophyComputer scienceSocial psychologyArtificial intelligence

Abstract

fetched live from OpenAlex

The article studies the conceptual metaphors of time in the sonnets of Shakespeare in light of the Conceptual Metaphor Theory (CMT) of Lakoff and Johnson (1980) presented in their book, Metaphors We Live By, and Kovecses’ (2002) informative views in his book, Metaphor: A Practical Introduction. The extracted metaphors selected from a variety of sonnets that tackle the theme of time will be divided into three sub-categories: structural, ontological, and orientational. Under ontological metaphors, the study addresses metaphors in the forms of personification, metonymy, and synecdoche. Using the cognitive approach to understand the abstract concept of time in terms of a variety of concrete concepts with experiential dimension enables the reader to perceive this concept from different perspectives. The study hopes to show that the cluster of source domains Shakespeare provides in the metaphors maps an association of multidimensional possibilities that improve our understanding of time. Also, this consortium of possibilities points to the creativity and the wide scope of Shakespeare’s vision. The study hopes to add another vantage point from which to view Shakespeare’s presentation of time in light of modern progress in the studies of conceptual metaphors and cognitive poetics.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score0.430

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.022
GPT teacher head0.290
Teacher spread0.268 · 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
Published2020
Admission routes1
Has abstractyes

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