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Record W2763056196 · doi:10.3389/fpsyg.2017.01793

The Longitudinal Contribution of Early Morphological Awareness Skills to Reading Fluency and Comprehension in Greek

2017· article· en· W2763056196 on OpenAlexaff
George Manolitsis, Ioannis Grigorakis, George K. Georgiou

Bibliographic record

VenueFrontiers in Psychology · 2017
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFluencyPsychologyPhonological awarenessReading comprehensionVocabularyReading (process)Rapid automatized namingComprehensionVerbal fluency testCognitionOrthographyWord recognitionCognitive psychologyLinguisticsDevelopmental psychologyNeuropsychologyMathematics education

Abstract

fetched live from OpenAlex

The purpose of this longitudinal study was to examine the role of three morphological awareness (MA) skills (inflection, derivation, and compounding) in word reading fluency and reading comprehension in a relatively transparent orthography (Greek). Two hundred and fifteen (104 girls; mean age = 67.40 months, at kindergarten) Greek children were followed from kindergarten (K) to grade 2 (G2). In K and grade 1 (G1), they were tested on measures of MA (two inflectional, two derivational, and three compounding), letter knowledge, phonological awareness, rapid automatized naming, and general cognitive ability (vocabulary and nonverbal IQ). At the end of G1 and G2, they were also tested on word reading fluency and reading comprehension. The results of hierarchical regression analyses showed that the inflectional and derivational aspects of MA in K as well as all aspects of MA in G1 accounted for 2% to 5 % of unique variance in reading comprehension. None of the morphological awareness skills predicted word reading fluency, after controlling for the effects of vocabulary and rapid automatized naming. These findings suggest that the MA skills, even when assessed as early as in kindergarten, play a significant role in reading comprehension development.

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 score0.058
Threshold uncertainty score0.507

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 head0.026
GPT teacher head0.355
Teacher spread0.330 · 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

Citations72
Published2017
Admission routes1
Has abstractyes

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