‘This is your brain on devices’: Media accounts of young children’s use of digital technologies and implications for parents and teachers
Bibliographic record
Abstract
Contemporary children are growing up in a post-typographic era, where mobile electronic devices and digital texts are increasingly present. For parents and educators, shifts into new digital practices and new text forms can create a sense of uncertainty. In response to parent and teacher interest, popular media have frequently focused on topics relating to young children and shifting digital practices. This study addresses popular media accounts of children and digital technologies over five years (2013–2018), looking in particular at the emergence of mobile devices and their impact on children’s changing literacy practices. The authors collected popular media articles over this time period and analysed them for the ways in which children and digital technologies were represented and these media called on teachers and parents to respond. The authors provide an overview of their findings and address key themes from the articles, sharing influential examples and addressing the implications and influences of media perspectives. Finally, the authors examine the implications of popular media accounts in relation to informing parent beliefs and approaches, and curriculum responses.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 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".