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Record W4311566348 · doi:10.18296/ecf.1110

Young children co-constructing stories with teachers

2022· article· en· W4311566348 on OpenAlexaboutno aff
Amanda White, Shelley Stagg Peterson

Bibliographic record

VenueEarly Childhood Folio · 2022
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsnot available
Fundersnot available
KeywordsSociocultural evolutionMeaning (existential)Embodied cognitionGestureConstruct (python library)Meaning-makingPsychologyGazeEarly childhood educationEarly childhoodDevelopmental psychologyUnderpinningPedagogySociologyLinguisticsEpistemology

Abstract

fetched live from OpenAlex

Children’s story experiences are foundational to their social, emotional, and communication development. Viewed through a sociocultural lens, variability in the ways children and teachers interact during stories across diverse learning contexts is expected. This article explores the social and cultural knowledge demonstrated by children of different ages as they co-construct meaning multimodally during stories with teachers across two early childhood educational settings, in Canada and New Zealand. Teachers’ gestures, gaze, questions, and verbal and non-verbal affirmations centred on themes, characters, and actions as they co-created stories with children. Teachers’ mediating roles and practices supported and sustained children’s embodied actions, languages, and cultures. Teacher–child stories shared in this article highlight the value of everyday stories as contexts for extending children’s learning, and the multimodal nature of story interactions underpinning the co-construction of meaning in early childhood education contexts.

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)0.005
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 score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0040.003
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.017
GPT teacher head0.310
Teacher spread0.293 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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