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Record W2068179811 · doi:10.5539/ijel.v2n1p231

Relationship between Providing Persian Equivalents of English Adjectives and Iranian EFL Learners' Active Vocabulary

2012· article· en· W2068179811 on OpenAlexvenueno aff
Shadi Afzal

Bibliographic record

VenueInternational Journal of English Linguistics · 2012
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPersianVocabularyEnglish as a foreign languageSample (material)Control (management)Test (biology)PsychologyMathematics educationEnglish languageForeign languageLinguisticsComputer scienceArtificial intelligenceChemistry

Abstract

fetched live from OpenAlex

This study attempted to find the relationship between providing Persian equivalents of English adjectives and Iranian EFL learners' active vocabulary. To this end, 100 students studying English as a foreign language at a Language Institute in Shiraz were asked to participate in this study. Participants were divided into experimental and control groups with 50 participants in each. The sample was homogenous with regard to their English level. To investigate the possible effects of providing Persian equivalents of English adjectives on Iranian EFL learners' active vocabulary, an independent Sample t-test was run on the vocabulary scores of 100 students. After analyzing the data it was found out the performances of all the participants in the “Experimental” group were better than those in the “Control” group.

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)0.009
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.041
GPT teacher head0.349
Teacher spread0.308 · 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
Published2012
Admission routes1
Has abstractyes

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Same venueInternational Journal of English LinguisticsSame topicSecond Language Acquisition and LearningFrench-language works237,207