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Record W1542572594 · doi:10.15353/joci.v10i2.2656

The Cancellation of the Community Access Program and the Digital Divide(s) in Canada: Lessons Learned and Future Prospects

2013· article· en· W1542572594 on OpenAlexaffvenueabout
Chris Blanton

Bibliographic record

VenueThe Journal of Community Informatics · 2013
Typearticle
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDigital divideDisadvantagedThe InternetInternet accessSituatedDevolution (biology)DisadvantageContext (archaeology)Government (linguistics)Public relationsLast mile (transportation)BusinessPolitical scienceTelecommunicationsInternet privacyEconomic growthSociologyComputer scienceWorld Wide WebGeographyMileEconomics

Abstract

fetched live from OpenAlex

The Government of Canada's recent termination of its Community Access Program eliminated a major source of funding for organizations that connect disadvantaged individuals and communities to the Internet. Nevertheless, inequalities in Internet access and usage continue to exist. In the absence of a coordinated national policy to address digital divides, responsibility for providing Internet access to low-income Canadians devolves primarily onto large civic and regional libraries. This devolution works to the particular disadvantage of remote and rural areas, which tend to have neither an affordable supplier of residential broadband nor the economic base to support library systems large enough to provide community access sites. For communities in this situation, the First Mile paradigm offers some hope. In the broader context, future Internet connectivity initiatives in Canada should look beyond simply providing "access," and link the individual's effective use of information and communication technologies to the well-being of the community in which the individual is situated.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.440
Threshold uncertainty score0.857

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.026
GPT teacher head0.271
Teacher spread0.245 · 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 designQualitative
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

Citations2
Published2013
Admission routes3
Has abstractyes

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