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Record W2964664326 · doi:10.1177/1463949119867400

‘This is your brain on devices’: Media accounts of young children’s use of digital technologies and implications for parents and teachers

2019· article· en· W2964664326 on OpenAlexafffund
Linda Laidlaw, Joanne O’Mara, Suzanna Wong

Bibliographic record

VenueContemporary Issues in Early Childhood · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsUniversity of Alberta
FundersSocial Sciences and Humanities Research Council of CanadaDeakin University
KeywordsDigital mediaCurriculumDigital literacyMedia literacyPsychologyNew mediaMobile deviceSociologyPedagogyComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.626

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.034
GPT teacher head0.298
Teacher spread0.264 · 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

Citations18
Published2019
Admission routes2
Has abstractyes

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