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Reading Rate in L1 Mandarin Chinese and L2 English Across Five Reading Tasks

2007· article· en· W2097379270 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueModern Language Journal · 2007
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsYork University
Fundersnot available
KeywordsMandarin ChineseReading (process)LinguisticsReading ratePsychologyReading comprehensionPhilosophy

Abstract

fetched live from OpenAlex

This study compared first and second language (L1/L2) reading rate and task performance on five tasks (scanning, skimming, normal reading, learning, memorizing) in two groups of Mandarin speakers (Canada group, China group). A repeated measures ANOVA design was used with one between‐subject factor (Group), two within‐subject factors (Language, Task), and L2 proficiency as a covariate. The results indicated substantial L1/L2 rate gaps for all tasks, but, for the most part, this gap was not the same across tasks. Comparing L1 and L2 task performance, the results indicated some decrease in L2 scores on three tasks (scanning, skimming, memorizing). Regarding group differences, the China group had faster reading rates on two L2 tasks (scanning, skimming) and on all L1 tasks; the Canada group scored higher on the memorizing task, a written recall. L2 proficiency was not a predictor of L2 reading rate but was a predictor of L2 performance on two tasks (learning, memorizing).

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.804
Threshold uncertainty score0.764

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 integrity0.0000.001
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 head0.008
GPT teacher head0.329
Teacher spread0.321 · 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