The Digital Divide in Canada and the Role of LEO Satellites in Bridging the Gap
Bibliographic record
Abstract
Overcoming the digital divide in rural and remote areas has always been a big challenge for Canada with its huge geographical area. In 2016, the Canadian Radio-television and Telecommunications Commission announced broadband Internet as a basic service available for all Canadians. However, approximately one million Canadians still do not have access to broadband services as of 2020. The COVID-19 pandemic has made the situation more challenging, as social, economic, and educational activities have increasingly been transferred online. The condition is more unfavorable for Indigenous communities. A key challenge in deploying rural and remote broadband Internet is to plan and implement high-capacity backbones, which are now available only in denser urban areas. For any Internet provider, it is almost impossible to make a viable business proposal in these areas. For example, the vast land of the Northwest Territories, Yukon, and Nunavuts diverse geographical features present obstacles for broadband infrastructure. In this paper, we investigate the digital divide in Canada with a focus on rural and remote areas. In so doing, we highlight two potential solutions using low Earth orbit (LEO) constellations to deliver broadband Internet in rural and remote areas to address the access inequality and the digital divide. The first solution involves integrating LEO constellations as a backbone for the existing 4G/5G telecommunications network. This solution uses satellites in a LEO constellation to provide a backhaul network connecting the 4G/5G access network to its core network. The 3rd Generation Partnership Project already specifies how to integrate LEO satellite networks into the 4G/5G network, and the Canadian satellite operator Telesat has already showcased this solution with one terrestrial operator, TIM Brasil, in their 4G network.
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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 | 0.002 | 0.006 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".