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Record W2979763772 · doi:10.1111/sjop.12578

The relationship between morphological awareness and reading comprehension in Spanish‐speaking children

2019· article· en· W2979763772 on OpenAlexafffund
Maria D’Alessio, Virginia Jaichenco, Maximiliano A. Wilson

Bibliographic record

VenueScandinavian Journal of Psychology · 2019
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversité Laval
FundersSocial Sciences and Humanities Research Council of CanadaUniversidad de Buenos AiresConsejo Nacional de Investigaciones Científicas y Técnicas
KeywordsPronunciationReading comprehensionPsychologyComprehensionLinguisticsReading (process)Cognitive psychology

Abstract

fetched live from OpenAlex

In the last decades, a series of studies has explored the role of morphological awareness on reading comprehension. Path analysis studies performed in English have shown that morphological awareness benefits reading comprehension both directly and indirectly, through word decoding. This issue has seldom been explored in Spanish. The aim of this study was to replicate in Spanish the results previously found in English. We used path analysis to assess three alternative models of the relationship between morphological awareness, word decoding and reading comprehension in 4th grade Spanish-speaking children. Contrary to English, we found that morphological awareness benefits reading comprehension only directly. We conclude that in Spanish, in which accurate and fluent pronunciation of written words can be achieved through grapheme-to-phoneme conversion rules, morphological awareness does not help the correct pronunciation of words. Thus, morphological awareness is not relevant for word decoding in Spanish but is related to reading comprehension since this type of morphological knowledge provides access to the semantic and syntactic information of new words.

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 score0.009
Threshold uncertainty score0.462

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 integrity0.0000.001
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 head0.052
GPT teacher head0.368
Teacher spread0.316 · 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

Citations13
Published2019
Admission routes2
Has abstractyes

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