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Delving into EFL Learners’ Cultural Conceptions Through Metaphor Analysis

2011· article· en· W1712557071 on OpenAlexvenueno aff
Mostafa Morady Moghaddam, Mina Gholamzadeh

Bibliographic record

VenueCross-cultural communication · 2011
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsnot available
Fundersnot available
KeywordsMetaphorConceptualizationTarget culturePsychologyEnglish cultureConceptual metaphorLinguisticsFactor (programming language)PedagogyComputer science

Abstract

fetched live from OpenAlex

Metaphors are the windows through which it is possible to see individuals’ particular way of thinking. In this paper, a case is made through investigating L2 learners’ metaphors conceptualizing English culture. The data elicited by using the prompt “English culture is like… because…” and the dominant metaphors were identified with reference to the frequency of each metaphor. In total, 67 conceptual metaphors were placed under 14 specific categories. These 14 categories were further divided into three broad categories as: 1) culture as a positive factor; 2) culture as a negative factor; and 3) culture as an unintelligible factor. Of particular interest is the use of positive metaphors to represent culture. The findings showed that the choice of metaphors is different regarding learners’ proficiency level. With beginners, English culture is mostly realized as a negative factor. However, in intermediate and advanced levels, learners are more optimistic about English culture and the dominating ideology is a positive view. Moreover, learners’ conceptualization of second-language culture was different regarding gender. Finally, Chi-square test was computed to find out whether the differences are meaningful. The findings of this article are useful for investigating, selecting, and teaching cultural aspects in EFL classes. Hence, the results are of interest for researchers, material developers, and teachers. Key words: Culture; SLA; Metaphor; Proficiency level; Gender; EFL learners

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.451
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.080
GPT teacher head0.394
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; both teacher heads agree on what is shown here.

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

Citations1
Published2011
Admission routes1
Has abstractyes

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