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Record W2943585670 · doi:10.1111/1467-9817.12135

Cross‐lagged panel analysis of reciprocal effects of morphological processing and reading in Chinese in a multilingual context

2018· article· en· W2943585670 on OpenAlexaff
Dongbo Zhang, Keiko Koda, Che Kan Leong, Elizabeth Pang

Bibliographic record

VenueJournal of Research in Reading · 2018
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsPsychologyReading (process)Reading comprehensionReciprocity (cultural anthropology)Context (archaeology)Path analysis (statistics)LinguisticsReciprocalComprehensionPanel analysisAffect (linguistics)Developmental psychologyPanel dataSocial psychologyCommunicationComputer science

Abstract

fetched live from OpenAlex

Background While much is known about how morphological awareness (MA) contributes to reading development, little attention has been paid to how reading may conversely affect MA development, particularly in readers of Chinese in a bilingual/multilingual setting. Methods The study adopted a cross‐lagged panel design. Young bilingual readers of Chinese were measured in MA, word reading and reading comprehension – all in Chinese – twice from Grade 3 to Grade 4. Results Path analysis revealed that Grade 3 MA significantly predicted Grade 4 reading comprehension after controlling for the autoregressive effect. Over and above Grade 3 MA, Grade 3 reading also significantly predicted Grade 4 MA. Subsequent analyses, however, revealed disparate developmental patterns between those with Chinese and English, respectively, as their home language. Implications These findings, while supporting reciprocity of developmental relationships between MA and reading, also suggested that the pattern of relationships can vary as a function of students' target language experiences in a bilingual/multilingual setting.

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)0.008
metaresearch head score (Gemma)0.003
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 score0.086
Threshold uncertainty score0.508

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.003
Science and technology studies0.0000.001
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 head0.091
GPT teacher head0.480
Teacher spread0.389 · 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.

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

Citations12
Published2018
Admission routes1
Has abstractyes

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