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Record W2247259066

Learning to Read in Two Languages: Impediment or Facilitator?

2011· article· en· W2247259066 on OpenAlexaffabout
Rhonda Joy

Bibliographic record

VenueElectronic Journal of Foreign Language Teaching · 2011
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsSpellingPseudowordFacilitatorReading (process)PsychologyPhonological awarenessMathematics educationLinguisticsPedagogyLiteracy
DOInot available

Abstract

fetched live from OpenAlex

The purpose of the study reported on in this paper was to investigate the concurrent development of firstand second-language reading and spelling skills. The study’s participants were 47 grade one children enrolled in a Canadian French-Immersion program and their first language was English. The following skills were tested at the beginning and end of the children’s school year: word reading, phonological awareness, oral-language proficiency, spelling and pseudoword-decoding. Descriptive statistics and ANOVAs revealed that there was an improvement in French and English reading and spelling skills from the beginning to the end of the year with a tendency for French skill development to be faster than English. English reading and spelling skills continued to develop throughout the year despite a lack of formal instruction in English. All English and French skills improved over time. However, the rate of development of some of the English skills was slower than what was reported in the standard scores for English-only students. Results suggested that learning in a second language proved to be both a facilitator and an impediment, depending on the particular skills examined.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.216
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.017
GPT teacher head0.341
Teacher spread0.324 · 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.

Study designQualitative
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

Citations1
Published2011
Admission routes2
Has abstractyes

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