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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 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.720
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0030.002
Open science0.0010.000
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 head0.010
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 teacher head, not a consensus.

Study designTheoretical or conceptual
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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