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Record W6959426252 · doi:10.7939/r3-gpg0-mj46

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

2020· dissertation· en· W6959426252 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Alberta Library · 2020
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Practices and Plant Genetics
Canadian institutionsnot available
Fundersnot available
KeywordsMeaning (existential)SituatedMindsetConstruct (python library)EthnographySocial constructivismSocial constructionismScholarshipLiteracy

Abstract

fetched live from OpenAlex

Situated within social constructivist understandings of multiliteracies, this eight-month ethnographic study explored transliteracy practices in a grade three classroom. The intention of this research was to conduct an ethnographic case study to understand how digital and multiliteracies support the ways children construct meaning through transliteracy practices in elementary school. 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 understandings of literacy: critical transliteracy, digital transliteracy, social transliteracy, and disciplinary transliteracy. Understandings and implications of a transliteracy mindset are articulated in their contributions to scholarship and pedagogy and through descriptive examples of transliteracy in the classroom. This study contributes to growing conceptual understandings of transliteracy and 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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.399
Threshold uncertainty score0.997

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 studies0.0000.000
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 head0.033
GPT teacher head0.227
Teacher spread0.194 · 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 routes1
Has abstractyes

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