Defining and Exploring Broadband Connections and Education Solutions in Canada’s North
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".