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Record W4409263943 · doi:10.29173/cjfy30140

Screen Time and Media Consumption: The Role of Technology in Childhood Development

2025· article· en· W4409263943 on OpenAlexaffvenue
Naturelle Sheppard

Bibliographic record

VenueCanadian Journal of Family and Youth / Le Journal Canadien de Famille et de la Jeunesse · 2025
Typearticle
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsMacEwan University
Fundersnot available
KeywordsScreen timeMedia consumptionConsumption (sociology)Early childhoodPsychologyBusinessDevelopmental psychologyAdvertisingSociologyMedicineSocial science

Abstract

fetched live from OpenAlex

For the last century, technology has actively shaped the childhoods of many generations and has become a fundamental aspect of childhood. From the introduction of the radio and television in the 1900s to digital technology and gaming platforms in the 21st century, children have constantly been exposed to various forms of technology that have aided their understanding of the world. Technology use is not inherently harmful, as its establishment and progression have contributed to a comprehensive understanding of childhood. Notably, the introduction of the internet has enabled national and global access to information, allowing individuals to gain valuable knowledge related to children's development from educated professionals. Further, the interconnectedness of social media facilitates the exchange of information worldwide, expanding an individual’s perspective and understanding of childhood. However, the rapid advancement of technology from the early modern world to the contemporary digital world has perpetuated issues associated with the overreliance on digital devices. Children’s unrestricted access to technology, in conjunction with the intensification of media consumption and screen time, is particularly concerning for children’s cognitive development and social interactions. It has raised public health concerns, threatening the healthy and normal development of children.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.336
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.006
GPT teacher head0.208
Teacher spread0.202 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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Same venueCanadian Journal of Family and Youth / Le Journal Canadien de Famille et de la JeunesseSame topicICT Impact and PoliciesFrench-language works237,207