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Record W1638010282 · doi:10.15353/joci.v10i2.2738

Building First Nation Owned and Managed Fibre Networks across Quebec

2014· article· en· W1638010282 on OpenAlexaffvenueabout
Tim Whiteduck, Brian Beaton

Bibliographic record

VenueThe Journal of Community Informatics · 2014
Typearticle
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsBusinessThe InternetPlan (archaeology)Service (business)Internet accessService providerConstruct (python library)Rural areaPublic relationsPublic administrationEconomic growthTelecommunicationsPolitical scienceMarketingEngineeringGeography

Abstract

fetched live from OpenAlex

In Canada, small rural and remote communities continue to struggle to access equitable and affordable high speed internet connections that address local priorities and needs. The First Nations Education Council (FNEC) is working with their community partners across Quebec to plan and operate a First Nation owned and managed fibre network to deliver broadband connections throughout each community. Public and private partnerships were established by FNEC to fund and construct the regional and local networks connecting these rural and remote communities. The paper describes the history of this development along with its future goals. Sharing infrastructure and network support services with all the other service providers (health, education, administration, justice, policing, homes, etc.) in each of these communities helps to sustain the ongoing operation and maintenance of the network.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.209

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.001
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.019
GPT teacher head0.252
Teacher spread0.233 · 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 designNot applicable
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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