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Record W2053111230 · doi:10.1093/applin/amu007

The Effects of Vocabulary Breadth and Depth on English Reading

2014· article· en· W2053111230 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueApplied Linguistics · 2014
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsQueen's UniversityUniversity of Toronto
Fundersnot available
KeywordsVocabularyReading (process)Reading comprehensionLinguisticsVocabulary developmentPsychologyComprehensionExtensive readingComputer science

Abstract

fetched live from OpenAlex

This study explored the relationship between two dimensions of vocabulary knowledge, that is, breadth of vocabulary (the number of words known) and depth of vocabulary (the richness of word knowledge), and their effects on different aspects of English reading in Chinese high school students learning English as a second language. Two hundred and forty-six Grade 8 students in China were administered measures of word reading, vocabulary breadth, vocabulary depth, and reading comprehension. Results showed that breadth and depth of vocabulary were moderately correlated. They both contributed to word reading, but breadth of vocabulary had a stronger effect than depth of vocabulary. When reading comprehension was the outcome measure, vocabulary breadth significantly predicted a multiple-choice reading comprehension measure, which requires general understanding of the text, while vocabulary depth contributed to summary writing, a measure of deeper text processing. Discussion focuses on the important roles of different dimensions of vocabulary knowledge for different types of second language reading.

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.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.954
Threshold uncertainty score0.385

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0000.000

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 head0.006
GPT teacher head0.253
Teacher spread0.247 · 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