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Record W4384161226 · doi:10.7202/1100571ar

“Unstoppable!”: Children as Readers and Researchers of Reading in an Arts‑Based Project

2023· article· en· W4384161226 on OpenAlexaffvenue
Danielle Fuller

Bibliographic record

VenueMémoires du livre · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsReading (process)ScholarshipDocumentationThe artsMultimodalityVernacularClass (philosophy)Childhood studiesSociologyVisual artsPedagogyPsychologyMedia studiesLiteratureLinguisticsPolitical scienceArtComputer scienceDevelopmental psychologyWorld Wide Web

Abstract

fetched live from OpenAlex

Historians of children’s reading highlight how the voices, opinions and ideas of actual children, are missing from most institutional archives. Contemporary scholars have an opportunity to change that situation for the future by co‑producing research with children. This paper examines how a class of ten‑year‑old schoolchildren in England engaged with a multidisciplinary arts project involving creative writing, digital game‑making and reading research activities. The paper builds upon scholarship that emphasizes the importance of conceptualizing research projects about children’s reading with reference to transliteracies, multimodality and creative reading. It argues that, by offering the children different forms and media of expression and various means of documentation, the project enabled them to articulate vernacular as well as schooled ways of reading. Significantly, the children’s perspectives on their own reading acts and habits offer valuable insights into the qualities of their reading experiences as child readers navigating a twenty‑first‑century transmedia environment.

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)0.011
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 score0.038
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0260.035
Scholarly communication0.0170.011
Open science0.0020.014
Research integrity0.0040.008
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 head0.095
GPT teacher head0.331
Teacher spread0.236 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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