Bibliographic record
Abstract
Contemporary childhood exists in a rapidly changing literacy context in the digital age, where digital devices and technology are progressively used at home (OECD 2019). In the global context, digital technology is greatly emerging in children’s lives, including the way of their play, learning, communicating and entertainment (Marsh et al. 2016). In this paper presentation, the author who is a researcher in the intersection of digital literacy and children’s literature, engages in autobiographical narrative inquiry (Clandinin and Connelly 2000), where she tells and retells her stories of reading contemporary fiction picture books, focusing on digital practices in both written text and illustrations. Such experiences are reflected and compared to her experiences of reading contemporary academic articles and reports on digital literacy studies in early childhood education. She noticed that there is a significant missing of digital lives and practices in contemporary fiction picture books, which contrasts with the prevalence of children’s digital usage in the contemporary post-typographic era. As children’s fiction is infused with ideology, the text instills values and beliefs in young readers, which, further, will shape children’s sociocultural development (Stephens 1992). For this reason, it is critical for future research to examine the ideological message and cultural discourse in relation to digital literacy practices in children’s fiction work. Because the gaps and differences between the ideologies of digital usage in children’s literature and children’s digital literacy practices reality could cause confusion to our young readers. This article highlights the silence of digital lives and practices in today’s fiction picture books and this research urges writers, illustrators, educators, and parents to pay attention to the gaps between digital practices in today’s children’s real lives and the contemporary fiction picture books.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".