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Record W2106237815 · doi:10.5430/wjel.v2n2p43

Avoidance of Phrasal Verbs in Learner English: A Study of Iranian Students

2012· article· en· W2106237815 on OpenAlexvenueno aff
Zargham Ghabanchi, Elahe Goudarzi

Bibliographic record

VenueWorld Journal of English Language · 2012
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsVerbLinguisticsPsychologyAffect (linguistics)Literal and figurative languageTest (biology)Significant differenceComputer scienceArtificial intelligenceMathematicsCommunication

Abstract

fetched live from OpenAlex

This study investigated the avoidance of English phrasal verbs by Iranian learners. It also investigated the role of phrasal verb types, types of measurement and level of English proficiency in any possible avoidance of phrasal verbs performed by Iranian learners of English. Two groups of Iranian learners (intermediate and advanced, a total of 85) took part in this study. The advanced learners were 35 MA students and Intermediate learners were 50 BA students of English at the Ferdowsi University of Mashhad. Both advanced and intermediate learners were randomly divided into three groups and three types of tests (multiple-choice, translation and recall) were taken to them which included phrasal verbs in two types (figurative and literal). Findings showed that test type and phrasal verb type had an effect on learners’ avoidance of phrasal verbs, but proficiency level did not affect learners’ performance. Therefore, it was concluded that the difference between L1 and L2 structure and semantic complexity of phrasal verbs might cause the learners’ avoidance.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.326
Teacher spread0.312 · 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

Citations16
Published2012
Admission routes1
Has abstractyes

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Same venueWorld Journal of English LanguageSame topicSecond Language Acquisition and LearningFrench-language works237,207