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Record W3047031224 · doi:10.25071/1916-4467.40577

Shifting Transliteracies in Elementary School: Understanding How Transliteracy Practices Contribute to Grade-3 Students’ Construction of Meaning

2020· article· en· W3047031224 on OpenAlexaffvenueabout
Jacqueline Filipek

Bibliographic record

VenueJournal of the Canadian Association for Curriculum Studies · 2020
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Media Use
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPedagogyMeaning (existential)MindsetSociologyConstruct (python library)SituatedLiteracyScholarshipPraxisMathematics educationPsychologyEpistemologyComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Situated within social constructivist understandings of multiliteracies, this eight-month ethnographic case study explored transliteracy practices in a Grade-3 classroom. The intention of this research was to learn how digital and multiliteracies support the ways in which children in elementary school construct meaning through transliteracy practices. Findings revealed that transliteracy, using both digital and analog technologies across modes, media, genres and platforms, is an effective lens to understand the shifting literacy practices of young 21st century learners. Transliteracy is described in relation to four literacy concepts: critical transliteracy, digital transliteracy, social transliteracy and disciplinary transliteracy. Understandings and implications of a transliteracy mindset are articulated in scholarship and pedagogy and by descriptive examples of transliteracy in the classroom. This study contributes to the growing conceptual understanding of transliteracy that supports the fluid nature of transliterate learning. It promotes the use of multiliteracies, student choice and opportunities to use more than one mode, device or platform simultaneously at school. Canadian students constantly face many choices in literacies; thus, being transliterate becomes significant to their literacy 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)0.002
metaresearch head score (Gemma)0.006
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 score0.583
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.103
GPT teacher head0.369
Teacher spread0.266 · 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

Citations0
Published2020
Admission routes3
Has abstractyes

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