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Record W2204733798 · doi:10.1093/pch/8.5.275

The digital divide – a new generation gap. Parental knowledge of their children's Internet use

2003· article· en· W2204733798 on OpenAlexaboutno aff
Catherine Swift, Anne Taylor

Bibliographic record

VenuePaediatrics & Child Health · 2003
Typearticle
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsThe InternetDigital divideInternet privacyMedicineComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

Canada is a world leader in terms of Internet penetration in households. Canadians use the Internet at home, in the workplace, at school and in public libraries. Youth are among the biggest users of media, including the Internet, and Internet access among youth is widespread. Young people are using the Internet for many purposes, including socializing through e-mail, instant messaging (IM) and chat rooms, and for entertainment, accessing information and homework. Along with the benefits of Internet use for children and youth, there are also risks. Young people can be exposed to pornographic, violent and hateful material, and come across age-inappropriate sites. They can also come into contact with people who may put them or their families at risk, or they can become the object of harassment. To find out how the Internet is influencing the lives of Canadian children, and the extent to which parents are aware of its hazards and influences, the Media Awareness Network (MNet) conducted two benchmark surveys, one in 2000 with parents (1), and the other in 2001 with children and youth themselves (2). The results from these surveys - and particularly the contrast between the two sets of findings - provide physicians, and others working with children and youth, with important food for thought about the Internet's potential impact on the healthy mental and physical development of this first generation of children and youth growing up with the Internet.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score0.530

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)0.0000.000

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 head0.021
GPT teacher head0.248
Teacher spread0.227 · 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

Citations4
Published2003
Admission routes1
Has abstractyes

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