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Record W4243169753 · doi:10.1037//0012-1649.36.1.66

Languages, scripts, and the environment: Factors in developing concepts of print.

2000· article· en· W4243169753 on OpenAlexaff
Ellen Bialystok, Tali Shenfield

Bibliographic record

VenueDevelopmental Psychology · 2000
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsYork University
Fundersnot available
KeywordsScripting languageHebrewPsychologyLinguisticsTask (project management)Meaning (existential)Neuroscience of multilingualismComputer science

Abstract

fetched live from OpenAlex

Preschool children bilingual in English and Hebrew were investigated for their understanding of concepts of print by means of two tasks. In the first, children had to understand that a printed word did not change its meaning if it moved to a new location. In the second, children had to make judgments about word length and ignore the size of the named objects. Previous research had shown bilingual French-English and Chinese-English children to excel in the first task, but only older Chinese-English bilinguals had an advantage in the second. The present study extended those results by investigating the effect of writing system in more detail. The study also examined the effect of the language of the environment by conducting parallel studies in environments in which either English or Hebrew was the community language. The results show that the bilingual children in both settings were more advanced than the monolinguals in both tasks and in both settings.

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)0.001
metaresearch head score (Gemma)0.008
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.023
GPT teacher head0.326
Teacher spread0.303 · 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

Citations6
Published2000
Admission routes1
Has abstractyes

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