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Record W2264896886

Reducing the Digital Divide in Rural Manitoba: A Proposed Framework

2014· article· en· W2264896886 on OpenAlexaffvenueabout
William Ashton, Roger A. Girard

Bibliographic record

VenueJournal of rural and community development · 2014
Typearticle
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsManitoba HealthBrandon University
Fundersnot available
KeywordsBroadbandDigital divideClosing (real estate)Social capitalRural areaBusinessBroadband networksTelecommunicationsInvestment (military)Economic growthPublic relationsMarketingEconomicsThe InternetPolitical scienceComputer scienceFinancePolitics
DOInot available

Abstract

fetched live from OpenAlex

Outside of larger centres across Canada public policies and corporate investments have left 2 million rural households under-served with broadband network capacity. After nearly 20 years of activities this gap may be closing, yet front line strategists suggest our rural digital divide will persist for years to come. A supplemental to the current approach is needed to connect these rural areas, yet it may require different technologies and social capital investment. This paper brings the reader inside a Manitoba working group (Forum) to reveal how applied research, assisted in formulating an integrated action framework that proposes an approach to provide access to users and expand the uses of broadband while calling for leadership and utilizing social marketing. New partnerships are envisioned across three levels of public sector, ISPs, and local businesses, along with youth and citizens. A proposed implementation framework is offered as an approach to incrementally provide specific rural broadband services to one area before moving to the next. This framework is not a silver bullet for all rural areas, but rather offers an approach that might be a useful starting point for under-served rural areas in Canada. Keywords: Rural broadband, under-served communities, access, availability, partnerships, integrated approach, implementation

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.836
Threshold uncertainty score0.349

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 integrity0.0000.001
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 head0.013
GPT teacher head0.221
Teacher spread0.209 · 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
Published2014
Admission routes3
Has abstractyes

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