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Record W2808871647 · doi:10.1017/s0142716418000127

The role of morphology in word naming in Spanish-speaking children

2018· article· en· W2808871647 on OpenAlexaff
Maria D’Alessio, Virginia Jaichenco, Maximiliano A. Wilson

Bibliographic record

VenueApplied Psycholinguistics · 2018
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversité LavalInstitut Universitaire en Santé Mentale de Québec
FundersSecretaría de Ciencia y Técnica, Universidad de Buenos AiresUniversidad de Buenos AiresConsejo Nacional de Investigaciones Científicas y Técnicas
KeywordsFluencyPsychologyPronunciationReading (process)Word recognitionGraphemeLinguisticsWord (group theory)Morphology (biology)Cognitive psychology

Abstract

fetched live from OpenAlex

ABSTRACT The role of morphology in word recognition during reading acquisition in transparent orthographies is a subject that has received little attention. The goal of this study is to examine the variables affecting the fluency and accuracy for morphologically complex word reading across grade levels in Spanish. We conducted two word-naming experiments in which morphological complexity and word frequency were factorially manipulated. Experiment 1 was a cross-sectional study with 2nd-, 4th- and 6th-grade children as participants. In Experiment 2, a longitudinal study, a sample of the children in 2nd and 4th grades in Experiment 1 were retested with the same stimuli 2 years later in order to explore the evolution of morphology and frequency effects. Analyses of reading latencies and accuracy in both experiments showed that grade and frequency affected both reading fluency and accuracy. Morphology only affected fluency, irrespective of grade. In accordance with previous literature in Italian, we conclude that when learning to read in transparent orthographies, morphology mostly benefits reading fluency since accurate pronunciation can be achieved through grapheme-to-phoneme conversion rules.

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)0.001
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 score0.483
Threshold uncertainty score0.631

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.010
GPT teacher head0.297
Teacher spread0.287 · 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

Citations16
Published2018
Admission routes1
Has abstractyes

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