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Record W2153391025 · doi:10.1177/1476718x12449453

Reading dual language books: Improving early literacy skills in linguistically diverse classrooms

2012· article· en· W2153391025 on OpenAlexaffabout
Rahat Naqvi, Keoma J. Thorne, Christina M. Pfitscher, David Nordstokke, Anne McKeough

Bibliographic record

VenueJournal of Early Childhood Research · 2012
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsUrduDual languageLiteracyReading (process)Extant taxonPsychologyMathematics educationPedagogyLinguistics

Abstract

fetched live from OpenAlex

Research has determined that dual language books have a positive effect on literacy achievement, motivation, and family involvement in children’s schooling. In this study we used quantitative methods to complement the largely qualitative extant research. We analyzed the early literacy skills of 105 kindergarten children (45 comparison, 60 treatment) with diverse language backgrounds (35% English, 31% Punjabi, 16% Urdu, 18% other languages) from eight kindergarten classes in four suburban Canadian schools. Statistical analyses indicated that children who were read to using dual language books, written in French, Punjabi, and Urdu, demonstrated significantly greater gains in graphophonemic knowledge than children who were read to in English only. This gain occurred specifically in children who spoke the targeted languages at home; children who did not speak the targeted languages were not negatively affected. Findings are discussed in terms of developing metalinguistic awareness and directions for practice and research are discussed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.030
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.377
Teacher spread0.352 · 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 source (direct Gemma or distilled Codex), 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

Citations61
Published2012
Admission routes2
Has abstractyes

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