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Record W2017642998 · doi:10.1177/0894439303256536

Beyond the Digital Divide in Canadian Schools

2003· article· en· W2017642998 on OpenAlexaffabout
E. Dianne Looker, Victor Thiessen

Bibliographic record

VenueSocial Science Computer Review · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicGender and Technology in Education
Canadian institutionsDalhousie UniversityAcadia University
Fundersnot available
KeywordsDigital divideInformation and Communications TechnologySocioeconomic statusPhysical accessRural areaAccess to informationPsychologySociologyInformation accessPolitical scienceComputer scienceDemographyLibrary scienceWorld Wide WebPopulation

Abstract

fetched live from OpenAlex

This article provides a descriptive analysis of issues related to the access and use of information and communication technology (ICT) among Canadian youth. In particular, it examines the extent to which inequities in the use of and access to ICT exist among Canadian high school students based on gender, socioeconomic status, and rural-urban location. The analyses suggest that there is a digital divide for Canadian youth in access to and experience with ICT. Rural youth are less likely to have access to computers in the home; however, frequency of use and perceived competency levels are not compromised because they make greater use of computers at school. Female youth and those from families with low levels of parental education are also less likely to have access to computers in their homes; they tend to access computers less frequently and report lower levels of computer skills competency.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.935
Threshold uncertainty score0.469

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.010
Science and technology studies0.0120.004
Scholarly communication0.0060.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.339
Teacher spread0.313 · 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 source (direct Gemma or distilled Codex), 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

Citations56
Published2003
Admission routes2
Has abstractyes

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