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Rapid naming speed components and reading comprehension in bilingual children

2011· article· en· W2146015248 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

VenueJournal of Research in Reading · 2011
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of AlbertaQueen's University
Fundersnot available
KeywordsPsychologyReading comprehensionArticulation (sociology)LinguisticsComprehensionReading (process)

Abstract

fetched live from OpenAlex

This study examined the development of rapid automatised naming (RAN) components (i.e. articulation time and pause time) in English and Chinese, and their relations to English reading comprehension, in Chinese English immersion students at Grades 2, 4 and 6. Results indicated that pause time rather than articulation time was highly correlated with total time in both English and Chinese in all three grades. English and Chinese articulation times and English and Chinese pause times were more related after Grade 2. All component times decreased with grade level, but the decrease in English pause time between Grades 2 and 4 was the greatest. English pause time explained variance in English reading comprehension only in Grade 6. There was little evidence of cross‐language transfer from Chinese RAN components to English reading comprehension.

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)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.095
Threshold uncertainty score0.531

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.186
GPT teacher head0.422
Teacher spread0.236 · 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