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Record W2965054774 · doi:10.3390/languages4030061

Exploring Learning Context Effects and Grapho(-Phonic)-Phonological Priming in Trilinguals

2019· article· en· W2965054774 on OpenAlexaff
Cíntia Avila Blank, Raquel Llama

Bibliographic record

VenueLanguages · 2019
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsWilfrid Laurier UniversityUniversity of Ottawa
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsLexiconLinguisticsComputer sciencePsychologyLanguage acquisitionLexical decision taskScope (computer science)Priming (agriculture)Natural language processingCognitionMathematics education

Abstract

fetched live from OpenAlex

A growing body of research on bilingual word recognition suggests that lexical access is language non-selective in nature. This claim aligns with the Dynamic Systems Theory (DST) approach to (multilingual) language acquisition, according to which complex systems involve a large number of elements that interact. In language learners, these interactions lead to the creation and dissolution of patterns as the tasks and environments around them change. In this study, we extend the scope from previous research on word recognition to include the role immersion plays on the transfer of grapho(-phonic)-phonological patterns among (Brazilian Portuguese–French–English) trilinguals. Two groups of participants—one group living in their L1 environment and the other in an L2 setting—were presented with a primed lexical decision task. Besides revealing a high impact of L2 immersion on the processing of grapho(-phonic)-phonological related primes, our results provide further support for the notion of language non-selective access to the lexicon, which seems to generalize to trilingual word recognition. Implications for the DST view of multiple language acquisition are briefly 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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.394
Threshold uncertainty score0.460

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.043
GPT teacher head0.314
Teacher spread0.271 · 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.

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

Citations2
Published2019
Admission routes1
Has abstractyes

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