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Record W4312647208 · doi:10.1177/23328584221131530

Classroom Promotion of Oral Language: Outcomes From a Randomized Controlled Trial of a Whole-of-Classroom Intervention to Improve Children’s Reading Achievement

2022· article· en· W4312647208 on OpenAlexaff
Sharon Goldfeld, Pamela Snow, Patricia Eadie, John Munro, Lisa Gold, Ha Le, Francesca Orsini, Beth Shingles, Judy Connell, Amy Watts, Tony Barnett

Bibliographic record

VenueAERA Open · 2022
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsRoyal Ottawa Mental Health Centre
FundersAustralian Research CouncilIan Potter Foundation
KeywordsReading (process)Intervention (counseling)Promotion (chess)Randomized controlled trialPsychologyMedical educationCluster randomised controlled trialAcademic achievementProfessional developmentMathematics educationMedicinePedagogyNursing

Abstract

fetched live from OpenAlex

Children need rich language learning experiences in school to build language and reading skills. Research suggests that various effective ways to support teacher provision of these experiences. The Classroom Promotion of Oral Language cluster randomized controlled trial ( n = 1,360 students; 687 intervention, 673 control) examined whether a teacher professional learning intervention targeting oral language in the first years of school led to improved student outcomes compared to usual teaching practices. The intervention comprised face-to-face professional learning and ongoing support. The primary outcome was student reading ability at Grade 3; secondary outcomes included oral language, reading, and mental health at Grades 1 and 3. No differences were detected between the intervention and control arms. Implications of results and future directions are explored.

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)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.019
GPT teacher head0.335
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
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

Citations14
Published2022
Admission routes1
Has abstractyes

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