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Record W3013057523 · doi:10.5539/elt.v13n4p91

Simile and Metaphor Interpretation in Children

2020· article· en· W3013057523 on OpenAlexvenueno aff
Yusi Song

Bibliographic record

VenueEnglish Language Teaching · 2020
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsnot available
Fundersnot available
KeywordsSimileMetaphorInterpretation (philosophy)Literal and figurative languageRelevance (law)PsychologyCategorizationRelevance theoryLinguisticsAnalogyCognitive psychologyEpistemologyCognitionPhilosophy

Abstract

fetched live from OpenAlex

Metaphors and similes have been treated as the same comparable types of figurative speech since Aristotle. In early theories, metaphors are interpreted as corresponding similes by paraphrasing. Based on this theoretical framework, some experimental studies interpret simile understanding as evidence for metaphor understanding. However, according to Relevance Theory, similes are interpreted as comparisons, whereas metaphors are interpreted as categorization statements. Therefore, only metaphor reveals metaphor interpretation. In other words, studies cannot use simile interpretation as evidence for metaphor interpretation ability in children. Simile should be easier to understand for children since they exclude an “ad hoc concept” construction as in metaphor understanding. This study seeks evidence showing children’s better performance in simile interpretation than that in metaphor interpretation, thus supporting Relevance Theory. For the Relevance Theory account, whether the experiments use similes or metaphors as testing materials is of considerable significance, whereas for comparison account, it is not. By review and re-interpretation of the empirical studies, we find that few early studies expose “real metaphor” understanding in children. Most experimental results indicate that simile interpretation is easier than metaphor interpretation for children. We consider comparison theory and the Relevance Theory as complementary strategies in metaphor interpretation.

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 score0.115
Threshold uncertainty score0.650

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)0.0010.000

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 head0.010
GPT teacher head0.271
Teacher spread0.262 · 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

Citations11
Published2020
Admission routes1
Has abstractyes

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