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Record W2218697551

A Map of Broadband Availability in Canada's Indigenous and Northern Communities: Access, Management Models, and Digital Divides (Circa 2009)

2010· article· en· W2218697551 on OpenAlexaboutno aff
Adam Fiser

Bibliographic record

VenueCommunication, politics & culture · 2010
Typearticle
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousGeographyCensusFraming (construction)BroadbandContext (archaeology)Environmental resource managementRegional scienceTelecommunicationsSociologyComputer sciencePopulationArchaeologyEcology
DOInot available

Abstract

fetched live from OpenAlex

In 2009, I participated in a project to map broadband availability ([> or =] 1.544 Mbps) in Canada's Indigenous and Northern communities, in collaboration with seven Regional Management Organizations (RMOs) partnered through Canada's First Nations SchoolNet (FNS) program. This article reports on the data collected to date, and provides an initial framing and exploration of that data in the context of Canada's national connectivity profile and several hypothesised access management models. I hypothesise that the management models, namely third-party commercial, Indigenous commercial, First Nations authority, and Indigenous social enterprise, shape broadband access in different ways, and also reflect different geographic conditions. I explore my hypotheses through a micro-census oriented methodology that reveals linkages between access management, geographic conditions, and digital divides among Canada's Indigenous and Northern regions.

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)0.000
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.199

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.009
Science and technology studies0.0040.001
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.230
Teacher spread0.217 · 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

Citations4
Published2010
Admission routes1
Has abstractyes

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