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Record W2105944914 · doi:10.1177/1468798414533562

Orchestrating literacies: Print literacy learning opportunities within multimodal intergenerational ensembles

2014· article· en· W2105944914 on OpenAlexaff
Lori McKee, Rachel Heydon

Bibliographic record

VenueJournal of Early Childhood Literacy · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsWestern University
Fundersnot available
KeywordsMultimodalityLiteracyCurriculumReading (process)Context (archaeology)PedagogyEthnographyPsychologyMultimodal therapySociologyMathematics educationLinguisticsComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

This exploratory case study considered the opportunities for print literacy learning within multimodal ensembles that featured art, singing and digital media within the context of an intergenerational programme that brought together 13 kindergarten children (4 and 5 years) with seven elder companions. Study questions concerned how reading and writing were practised within multimodal ensembles and what learning opportunities were afforded to the children while the participants worked through a chain of multimodal projects. Data were collected through ethnographic tools in the Rest Home where the projects were completed and in the children’s classroom where project content and tools were introduced and extended by the classroom teacher. Themes were identified through the juxtaposition of field texts in a multimodal analysis. The results indicate that the multimodality of the projects and the reciprocal intergenerational relationships forged in and through text-making afforded children opportunities to improvise and refine their print literacy practices as part of multimodal ensembles. The study is designed to contribute to the nascent, yet growing, body of knowledge concerning print literacy practices and learning opportunities as conceptualized within multimodal literacy and intergenerational curricula.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.694
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0020.004
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.259
Teacher spread0.230 · 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.

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

Citations37
Published2014
Admission routes1
Has abstractyes

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