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Record W2908309346 · doi:10.4324/9781315644851-2

Digital media and young children’s learning

2017· book-chapter· en· W2908309346 on OpenAlexaboutno aff
Natalia Kucirkova, and Jenny Radesky

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldComputer Science
TopicDigital literacy in education
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyComputer science

Abstract

fetched live from OpenAlex

This chapter focuses on typically developing children aged 0–2 years, referred to as infants, and four types of digital media: video, TV, tablets and electronic toys. For infants, anxieties around digital media use are most pronounced and vividly demonstrated in research and policy–making discussions. Considering the rapid uptake of digital media by families in the Minority World such as the UK, Australia, USA and Canada, it is important to draw together the current available evidence on the affordances and limitations of digital media for infants and reflect on how to best support parents and educators in their decision-making around their use. The chapter reviews the evidence on how infants learn more generally, followed by the key theoretical conceptualisations of children’s learning. There are various ways to describe how children learn; and given the idiosyncratic nature of development for each child, there are probably as many ways as there are children in the world.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0040.003
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.002

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 head0.010
GPT teacher head0.224
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations6
Published2017
Admission routes1
Has abstractyes

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Same topicDigital literacy in educationFrench-language works237,207