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Record W1969537416 · doi:10.5539/ass.v9n15p115

The Metaphoric Concept of XORDAN ‘To Eat’ in Persian

2013· article· en· W1969537416 on OpenAlexvenueno aff
Zahra Khajeh, Imran Ho Abdullah

Bibliographic record

VenueAsian Social Science · 2013
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsnot available
Fundersnot available
KeywordsConceptualizationPersianPsychologyLinguisticsMetaphorConceptual metaphorVerbFocus (optics)Cognitive linguisticsCognitionCognitive psychologyPhilosophy

Abstract

fetched live from OpenAlex

The main purpose of this study is to investigate the cognitive-semantic content of xordan in Persian, and whether it demarcates similar conceptual domain as the English verb ‘to eat’. The verbs related to the bodily experience of eating or consuming food is the source of metaphorical conceptualizations and mappings in various semantic domains rooted in universal experiential realities. Cross linguistic, cross cultural studies have reported both commonalities and variations in the conceptualization of the act of eating. Adopting the basic tenets of the Conceptual Metaphor Theory, proposed by Lakoff and Johnson (1980, 1999), this study is an attempt to delve into the conceptual system of Persian in order to explore its specific cultural embodiment, and socio-cultural influences in the use of metaphorical concepts of xordan. With a focus on the basic syntax and semantic properties of xordan, this study employs a lexical structure, i.e. the radial category in a chaining model to illustrate the complexities of metaphorical extensions of eating in Persian. Our observations reveal that the metaphorical expressions of the verb of xordan ‘to eat’ occur extensively in Persian, manifesting the Persians’ unique way of thinking and mind. These particular em-minded cultural models have widely left their traces in the Persians’ belief systems, the effects therefore, have been extended into Persian metaphoric language and cognitive conceptualizations.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.773
Threshold uncertainty score0.611

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.015
GPT teacher head0.303
Teacher spread0.288 · 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 designOther design
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

Citations2
Published2013
Admission routes1
Has abstractyes

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