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One complicated extended family: the influence of alphabetic knowledge and vocabulary on phonemic awareness

2011· article· en· W1562307116 on OpenAlexaff
Gene P. Ouellette, Allyson Haley

Bibliographic record

VenueJournal of Research in Reading · 2011
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsMount Allison University
Fundersnot available
KeywordsPhonemic awarenessPhonological awarenessPsychologyVocabularyLiteracyVocabulary developmentPhonologyCognitive psychologyLinguisticsTeaching methodMathematics educationPedagogy

Abstract

fetched live from OpenAlex

This research evaluated possible sources of individual differences in early explicit, smaller segment phonological awareness. In particular, the unique contributions of oral vocabulary and alphabetic knowledge to phonemic awareness acquisition were examined across the first year of school. A total of 57 participants were tested in kindergarten (mean age 5 years, 8 months) and again 1 year later midway through Grade 1. Results revealed that oral vocabulary and alphabetic knowledge were correlated with concurrent larger segment phonological awareness and phonemic blending in kindergarten whereas oral vocabulary was the only measure that predicted unique variance in phonemic awareness into Grade 1. Further, this pattern of results was most pronounced for analytic (segmenting), as opposed to synthetic (blending), phonemic awareness. These results highlight the importance of different component processes to explicit, smaller segment awareness depending upon the developmental period under study and also accentuate the need to separate analytic from synthetic phonemic awareness in literacy research.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.180
GPT teacher head0.438
Teacher spread0.257 · 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 source (direct Gemma or distilled Codex), 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

Citations47
Published2011
Admission routes1
Has abstractyes

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