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Record W4310436358 · doi:10.21432/cjlt28262

Defining and Exploring Broadband Connections and Education Solutions in Canada’s North

2022· article· en· W4310436358 on OpenAlexafffundvenueabout
Tammy Soanes-White

Bibliographic record

VenueCanadian Journal of Learning and Technology · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsAthabasca UniversityAurora College
FundersBeijing Normal UniversityAthabasca University
KeywordsGeospatial analysisInternet accessBroadbandDigital divideUploadTelecommunicationsThe InternetVariety (cybernetics)Computer scienceDownloadDistance educationWorld Wide WebGeographyRemote sensingSociology

Abstract

fetched live from OpenAlex

The use of technology and need for connection across distance permeates all education environments; nowhere is this more important than in Canada’s Northwest Territories. Broadband and telecommunications issues within the Northwest Territories are complex due to its vast geographical area and community dispersion, making connectivity and accessibility inconsistent. Due to these conditions, the North relies on a variety of broadband solutions to improve Internet speeds and access to education at a distance. This paper analyzes the impacts that broadband capacity and Internet access have on remote education by examining geographic information system data, which offers a framework that connects spatial and temporal data to analyse accessibility of remote education. Characteristics such as spatial location of communities, infrastructure (road systems), and the overlay of various broadband options will illustrate constraints and (dis)connectivity in various regions and inform readers about the complexity of remote connections. Analysis of current upload and download speeds from various regions and their impact on access to education supports geospatial data and analysis that the digital divide in remote regions of Canada has increased and is widening. Improving equitable access to postsecondary education will require a greater reliance on technology-enabled practices to improve learning opportunities.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.401

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.028
GPT teacher head0.201
Teacher spread0.174 · 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

Citations1
Published2022
Admission routes4
Has abstractyes

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