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Record W2082852594 · doi:10.5539/ass.v6n8p81

Patterns of Persian EFL Learners’ Comprehension of Idiomatic Expressions: Reading Strategies and Cross-Cultural Mappings in focus

2010· article· en· W2082852594 on OpenAlexvenueno aff
Bahador Sadeghi, Hossein Vahid Dastjerdi, Saeed Ketabi

Bibliographic record

VenueAsian Social Science · 2010
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsnot available
Fundersnot available
KeywordsPersianReading comprehensionLinguisticsComprehensionReading (process)Context (archaeology)PsychologyFocus (optics)Construct (python library)Interpretation (philosophy)MetacognitionComputer scienceCognitionHistory

Abstract

fetched live from OpenAlex

This paper primarily focuses on the description of the results of a study conducted with sixty Iranian adult EFL learners to investigate how the reading strategies and pragmatic elements are likely to govern and characterize the comprehension and interpretation process of English idioms with and without contextualized reading. It also intends to determine the role of cultural mappings and the extent to which Iranian EFL learners' knowledge of cultural idioms is affected by their L1 when they try to construct their own meanings. The researchers came up with some interesting inferences about such idiomatic expressions by the use of descriptive statistics and analyzing the participants' metacognitive comments in four phases. Keywords: Idiom, Comprehension, Reading strategies, Context, Culture mapping

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.344
Teacher spread0.319 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations10
Published2010
Admission routes1
Has abstractyes

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