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Record W2498408350 · doi:10.1075/hsld.1.17naq

Fostering early literacy learning using dual language books

2013· book-chapter· en· W2498408350 on OpenAlexaff
Rahat Naqvi, Anne McKeough, Keoma J. Thorne, Christina M. Pfitscher

Bibliographic record

VenueHamburg studies on linguistic diversity · 2013
Typebook-chapter
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSociocultural evolutionLiteracyReading (process)Language acquisitionFunction (biology)PedagogyPsychologyLinguisticsMathematics educationSociology

Abstract

fetched live from OpenAlex

In light of the importance of early literacy achievement to long term academic and economic success, educators urgently need to comprehend the sociocultural complexities manifested in learning contexts that involve multilingual students. Dual language books (DLBs; i.e., books written in English and another language) are one tool that can be used to examine these complexities as they allow teachers and students to identify and express cultural and linguistic assets and, in so doing, access and benefit from the cultural and linguistic capital of multilingual learners. As such, DLBs allow first languages to function as a cultural amplifier (i.e., a culturally invented tool or technology that advances development). In this chapter we present vignettes from teaching sessions in which researchers and volunteers focus on linguistic and cultural diversity while reading DLBs with first grade students. Our analyses of the sessions highlight children’s engagement in the literacy experiences, application of cultural knowledge, and growing metalinguistic awareness.

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)0.001
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 score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.085
GPT teacher head0.342
Teacher spread0.257 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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