Shifting Transliteracies in Elementary School: Understanding How Transliteracy Practices Contribute to Grade-3 Students’ Construction of Meaning
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".