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Record W2758178494 · doi:10.3968/9837

A Discussion of Color Metaphors From the Perspective of Cognition and Culture

2017· article· en· W2758178494 on OpenAlexvenueno aff
Weihua Yu

Bibliographic record

VenueStudies in literature and language · 2017
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsnot available
Fundersnot available
KeywordsMetaphorPerspective (graphical)Object (grammar)LinguisticsPerceptionColor termPsychologyPhenomenonConceptual metaphorSociologyAestheticsEpistemologyArtPhilosophyVisual arts

Abstract

fetched live from OpenAlex

Metaphor is a pervasive phenomenon which has traditionally been considered as a figure of speech used for special effects in a speech or an essay. It is pervasive in everyday life. Color terms are usually used to depict the colors of objects in the world. Every object in the world has its own color. There are a large number of metaphorical expressions with color serving as the source domain in both English and Chinese. As a very important human experience, colors have attracted many scholars attention. The study revealed that possible reasons for the similarities of color metaphor in the two languages can be attributed to the common perceptual and cultural experience, while the dissimilarities originated from the different living environment, religion, custom, and philosophy etc. This essay makes a comparison of color metaphor about the similarities and differences between English and Chinese. Understanding similarities and differences of color metaphor between English and Chinese is of great importance in the cross-cultural communication. It’s beneficial for us to do English teaching, English translation, and appreciation of English culture.

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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.199

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 head0.021
GPT teacher head0.353
Teacher spread0.332 · 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

Citations6
Published2017
Admission routes1
Has abstractyes

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