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Record W4414451595 · doi:10.1177/14687984251380969

Cartographies of voice: Children’s multimodal literacies, agency, and identity in public pedagogy

2025· article· en· W4414451595 on OpenAlexafffundabout
Anne Burke, Benjamin Boison

Bibliographic record

VenueJournal of Early Childhood Literacy · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsMemorial University of Newfoundland
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsExhibitionIdentity (music)ReflexivitySituatedNarrativeLiteracyEarly childhood educationMultimodalityNarrative inquiry

Abstract

fetched live from OpenAlex

This study explores how mapmaking, as an arts-based and culturally responsive pedagogical practice, supports young children’s voice, agency, and identity construction within classroom and public gallery contexts. We focus on 22 children (ages 6–7) in the early years of primary school, situated within the early childhood education life phase, whose multimodal mapping activities culminated in a curated exhibition at The Rooms —Newfoundland and Labrador’s provincial art gallery. Data included video transcripts of children’s narrative map sharing, teacher interviews, and field observations. Using reflexive thematic analysis informed by sociomaterial and multimodal literacy frameworks, we found that children’s maps functioned as cultural texts, expressing personal geographies through images, spatial arrangement, gesture and narration. Public exhibition recontextualized these artifacts as civic texts, validating children’s knowledge and affirming cultural identities—particularly for newcomer families. This study contributes to early childhood literacies and public pedagogy scholarship, illustrating how gallery curation can foster cultural affirmation, relational pedagogy, and civic participation within early education.

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 score0.681
Threshold uncertainty score0.699

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 communication0.0010.004
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.293
Teacher spread0.281 · 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

Citations3
Published2025
Admission routes3
Has abstractyes

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