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Beyond language borders: orthographic processing and word reading in Spanish–English bilinguals

2011· article· en· W1924607882 on OpenAlexaff
S. Hélène Deacon, Xi Chen, Yang Luo, Gloria Ramírez

Bibliographic record

VenueJournal of Research in Reading · 2011
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsThompson Rivers UniversityInstitute for Christian StudiesUniversity of TorontoDalhousie University
Fundersnot available
KeywordsReading (process)Orthographic projectionPsychologyLinguisticsOrthographyScripting languageComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

We present the results of an empirical test of the hypothesis that transfer of orthographic processing to reading occurs when the scripts under acquisition are written with the same unit (specifically, the same alphabet). We tested 97 Spanish–English bilingual children in Grades 4 and 7. We measured mother's education level, verbal and nonverbal abilities, rapid automatised naming and phonological awareness as control variables, as well as orthographic processing and reading in both of the children's languages. First, there was a relationship between orthographic processing and reading within both English and Spanish reading, a finding that is novel for the more transparent script of Spanish. Second, orthographic processing assessed in Spanish was related to English reading, even after substantive controls. This pattern of results offers support for the idea that orthographic processing transfers to reading across languages, when the scripts are written with the same unit.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.079
GPT teacher head0.430
Teacher spread0.351 · 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

Citations42
Published2011
Admission routes1
Has abstractyes

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