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Record W2157304057 · doi:10.5539/ijps.v3n2p48

Self-Esteem and Use of the Internet among Young School-Age Children

2011· article· en· W2157304057 on OpenAlexvenueno aff
Genevieve Marie Johnson

Bibliographic record

VenueInternational Journal of Psychological Studies · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsnot available
Fundersnot available
KeywordsInstant messagingSelf-esteemThe InternetInstantPsychologyContext (archaeology)Social psychologyPeer influencePeer groupDevelopmental psychologyWorld Wide Web

Abstract

fetched live from OpenAlex

The literature suggests a relationship between technology use and self-esteem. Such research has failed toconsider young school-aged children and their use of the internet, particularly across contexts. Thirty-eightchildren aged 6 to 8 years rated the level and nature of their internet use (email, instant message, play games,visit websites) at home, school and in the community (i.e., at someone else’s house). They also rated items thatmeasured home, school and peer self-esteem. Instant messaging at school explained 21% of the differences inschool self-esteem. As children tended to report instant messaging at school, they also tended to report thehighest school-based self-esteem. Instant messaging at someone else’s house explained 11% of the differences inhome self-esteem. As children tended to report instant messaging at someone else’s house, they also tended toreport the lowest home self-esteem. Visiting websites at someone else’s house explained 10% of the differencesin peer self-esteem. As children tended to report visiting websites at someone else’s house, they also tended toreport the highest peer self-esteem. Internet use during the early school years is related to children’s sense of selfand mediated by context.

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)0.002
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 score0.004
Threshold uncertainty score0.370

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.087
GPT teacher head0.380
Teacher spread0.293 · 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

Citations12
Published2011
Admission routes1
Has abstractyes

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