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Record W2113273662 · doi:10.5539/ijel.v4n6p113

Study on the Validity of bilingual Mandarin Version of Vocabulary Size Test

2014· article· en· W2113273662 on OpenAlexvenueno aff
Yu Wang, Wanyi Du

Bibliographic record

VenueInternational Journal of English Linguistics · 2014
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsMandarin ChineseTest (biology)VocabularyConsistency (knowledge bases)Internal consistencyPsychologyIndex (typography)LinguisticsNatural language processingStatisticsArtificial intelligenceComputer scienceMathematicsPsychometricsPhilosophy

Abstract

fetched live from OpenAlex

Vocabulary Size Test (VST) is a test to measure learners’ receptive vocabulary size by Paul Nation. Beside English-English version, VST has other five bilingual versions. This article is to describe the validation of its Mandarin version, i.e., English-Chinese version. Data shows that the bilingual English-Chinese version VST’s overall difficulty index, discriminating power, and internal consistency are all statistically satisfactory. One-way ANOVA test and Post Hoc test evidenced that the English-Chinese version VST can differentiate learners from different levels with high validity.

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)0.127
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.379
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.127
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.027
GPT teacher head0.335
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 teacher head, not a consensus.

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

Citations2
Published2014
Admission routes1
Has abstractyes

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