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Record W4408646447 · doi:10.1177/1086296x251318428

Running Around in the Story: Un/Raveling Compositions of Narrative Play

2025· article· en· W4408646447 on OpenAlexafffund
Ronna Mosher, Kimberly Lenters, Jennifer MacDonald

Bibliographic record

VenueJournal of Literacy Research · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsUniversity of ReginaUniversity of Calgary
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsNarrativePosthumanEmbodied cognitionComposition (language)LiteracySociologyNarrative inquiryStory tellingStorytellingAestheticsDynamics (music)PsychologyLiteratureArtEpistemologyPedagogy

Abstract

fetched live from OpenAlex

In this article we examine young children's outdoor narrative play as animated by the possibilities of running. Guided by posthuman perspectives, the provocations of sociomateriality, and the capacities of mycelial networks, we consider how stories and storying might occur in and as movement. We draw on interdisciplinary understandings of moving lines to map, analyze, and reimagine the compositional dynamics of two assemblages of outdoor narrative play. Following the compositional lines of these assemblages, we reencounter narrative play as embodied and moving occurrences in the indeterminate, overlaying, and un/raveling movements of a relational world. We come to see stories as other than objects of uniquely human composition, but rather as material, authoring subjects entangled in the unfolding of children's literacies and the dynamics of narrative play. We suggest attention to and engagement in such understandings as part of the living lines though which literacy education might newly tell stories about stories.

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)0.002
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 score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.010
Scholarly communication0.0060.006
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.093
GPT teacher head0.423
Teacher spread0.330 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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