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Reflections on Phonological Working Memory, Letter Knowledge, and Phonological Awareness: A Reply to Hartmann (2008)

2008· article· en· W2056851481 on OpenAlexaff
Susan Rvachew, Meghann Grawburg

Bibliographic record

VenueJournal of Speech Language and Hearing Research · 2008
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsMcGill University
Fundersnot available
KeywordsPhonological awarenessWorking memoryPsychologyPerceptionReciprocalCognitive psychologyVocabularySpeech perceptionPhonologyBaddeley's model of working memoryPhoneticsShort-term memoryCognitionLinguisticsLiteracy

Abstract

fetched live from OpenAlex

Purpose S. Rvachew and M. Grawburg (2006) found that speech perception and vocabulary skills jointly predicted the phonological awareness skills of children with a speech sound disorder. E. Hartmann (2008) suggested that the Rvachew and Grawburg model would be improved by the addition of phonological working memory. Hartmann further suggested that the link between phoneme awareness and letter knowledge should be modeled as a reciprocal relationship. In this letter, Rvachew and Grawburg respond to Hartmann’s suggestions for modification of the model. Method The literature on the role of phonological working memory in the development of vocabulary knowledge and phonological awareness was reviewed. Data presented previously by Rvachew and Grawburg (2006) and Rvachew (2006) were reanalyzed. Results The reanalysis of previously reported longitudinal data revealed that the relationship between letter knowledge and specific aspects of phonological awareness was not reciprocal for kindergarten-age children with a speech sound disorder. Conclusions Phonological working memory, if measured so that relative performance levels do not reflect differences in articulatory accuracy, may not alter the model because of its close correspondence with speech perception skills. However, further study of the hypothesized causal relationships modeled by Rvachew and Grawburg (2006) would be valuable, especially if experimental research designs were used.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.598
Threshold uncertainty score0.586

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.188
GPT teacher head0.455
Teacher spread0.266 · 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

Citations5
Published2008
Admission routes1
Has abstractyes

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