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Record W4210697229 · doi:10.1080/1475939x.2021.1997794

Literacies in the Making: exploring elementary students’ digital-physical meaning-making practices while crafting musical instruments from recycled materials

2022· article· en· W4210697229 on OpenAlexafffundabout
Michelle Schira Hagerman, Megan Cotnam-Kappel, Julie-Anne Turner, Janette Hughes

Bibliographic record

VenueTechnology Pedagogy and Education · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsOntario Tech UniversityUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsEmbodied cognitionMeaning (existential)Meaning-makingSituatedPedagogyMusicalSociologyMaking-ofPsychologyMathematics educationVisual artsComputer scienceArtAdvertising

Abstract

fetched live from OpenAlex

This study describes the literacies practices of three bi/multilingual fifth-grade students attending a French-language school in an urban community in Canada during a digital-physical Maker activity that included three phases: Plan Making, Instrument Making and Multimodal Making. Framed by theories of new literacies and embodied/sensory literacies, the authors captured students' first-person gaze with video spy glasses to inform understandings of their meaning-making practices in activity. Evidence suggests that students' Making was shaped by the ways that they understood themselves in relation to others, to materials, and to the expectations of schooling. Given the embodied meanings situated in the musical instruments that students made and the diverse, dynamic, discursive nature of students' literacies practices while Making at every phase, teachers are encouraged to adopt non-linear conceptualisations of Maker projects, and pedagogies that are active, responsive, and place value on the embodied meaning-making processes that bi/multilingual youth can use.

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)0.000
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.580

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.091
GPT teacher head0.360
Teacher spread0.269 · 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 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

Citations9
Published2022
Admission routes3
Has abstractyes

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