Navigating New Digital Divides in Early Literacy Instruction
Bibliographic record
Abstract
Mobile electronic devices and digital technologies are occupying an increas- ingly prominent place in contemporary childhood experiences across the globe. Rapid changes in digital tools and innovations require increased and different literacy skills for young children. Such shifts also present challenges to early years educators and children's families. Issues related to equitable access, pedagogically and developmentally sound learning structures, and suspicion of digital tools are often part of the complex contexts that surround and intersect emerging practices. Our paper will examine some of the 'new digital divides' emerging in early childhood, using multilayered data from a four year Canadian and Australian research project: interviews of Canadian and Australian early years educators; interviews of young children in their homes; analyses of curriculum and policy; and reports from popular media aimed at parents and teachers. The findings of this project reveal 'digital divides' that challenge public and popular perceptions of who is using digital tools and modes and who is not. The paper will address the following questions: How are digital mobile devices being taken up by young children at home and at school in Canadian and Australian contexts? What barriers exist at school and in children's homes? And finally, how can education systems and policies help to build in provision and support for digital literacy practices, and bridge practices across children's learning environments?
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.015 | 0.024 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".