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Record W4405057865 · doi:10.1007/978-3-031-69362-5_54

Parenting and Screens During Middle Childhood

2024· book-chapter· en· W4405057865 on OpenAlexaff
David S. Bickham, Drew P. Cingel, Amy I. Nathanson, Chad A. Rose, Colleen Russo Johnson, Erica Scharrer

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPsychologyMediationSocializationDevelopmental psychologySocial mediaEarly childhoodMaturity (psychological)Social psychologyPolitical science

Abstract

fetched live from OpenAlex

Abstract As their children reach middle childhood (6–12), parents are faced with new media-use challenges driven by their children’s development and growing independence. These youth spend over 5 ½ hours a day on screens, potentially displacing sleep, physical activity, and reading. During this stage, young people’s social lives evolve as the importance of peer interactions grows, their ability to understand the perspectives of other people develops, and their desire for online connection increases. In response, parents seek answers to questions concerning when and how to provide personal access to smartphones and social media. Research evidence supports considering a child’s needs, maturity, and responsibilities instead of only their age when providing them with a phone. Restrictive parental mediation strategies such as delaying access to social media during middle childhood may reduce overall use and limit risk for negative online social experiences, but they are also likely to interfere with the development of online social competencies and positive opportunities afforded by online socialization. A parental mediation approach that combines warmth and support with clear and consistent rules and consequences is likely protective, both against the negative impact of specific media content as well as against problematic media use overall.

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 categoriesnone
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 score0.672
Threshold uncertainty score0.662

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.241
Teacher spread0.215 · 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 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

Citations1
Published2024
Admission routes1
Has abstractyes

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