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Record W2780110161 · doi:10.1177/1468798417740788

Helping out, signing up and sitting down: The cultural production of “read-alouds” in three kindergarten classrooms

2017· article· en· W2780110161 on OpenAlexaff
Lyndsay Moffatt, Rachel Heydon, Luigi Iannacci

Bibliographic record

VenueJournal of Early Childhood Literacy · 2017
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsTrent UniversityWestern UniversityUniversity of Prince Edward Island
Fundersnot available
KeywordsLiteracyPedagogyPsychologyCurriculumEarly childhood educationReading (process)Early childhoodTransformational leadershipTeaching methodIdeologyDevelopmental psychologyMathematics educationSocial psychologyLinguisticsPolitical science

Abstract

fetched live from OpenAlex

Reading aloud to children is a ubiquitous practice in early childhood settings. While there are many recommendations for how educators should conduct these experiences, little research in the past decade has examined how read-alouds are actually accomplished. Using anthropological and sociological theories of learning, literacy and research, our analysis illustrates how read-alouds are enacted in three kindergarten classrooms. Our analysis highlights similarities and differences in how these phenomena are produced and raises questions about the consequences current ideologies of literacy learning may have for young children’s understandings of reading and of themselves as readers. Differences amongst the research sites are discussed in light of Cummins’ continuum of coercive and transformational curricula.

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)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0140.022
Scholarly communication0.0090.002
Open science0.0030.007
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0020.001

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 head0.024
GPT teacher head0.314
Teacher spread0.289 · 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 designQualitative
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

Citations3
Published2017
Admission routes1
Has abstractyes

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