Assessing the impacts of low-earth orbital satellite systems in remote indigenous communities: Social and economic outcomes of use in northern Canada
Bibliographic record
Abstract
Despite significant efforts to enhance digital connectivity in Canada's Far North, connectivity issues persist, particularly in small rural/remote communities. The recent emergence of Low-Earth Orbit (LEO) satellites may present one solution due to their lower latency, higher bandwidth, and potentially reduced costs when compared to traditional geostationary satellites. However, available primary data on user experiences with LEO satellite services remains limited. In this context, we present the results of an empirical study exploring the early-stage impact of LEO satellite technologies in two remote communities in the Northwest Territories (NWT) of Canada. Drawing from digital divide and digital inclusion research, we demonstrate how the introduction of new satellite technologies impacts first- and third-level digital divides in geographically remote, small population communities. Our study found that compared to other available satellite services, subscribers to Starlink's LEO satellite services experienced better internet speed, reliability, and cost-effectiveness, with notable improvements in digital access and engagement in online activities. This illustrates how LEO services may contribute to digital inclusion by supporting the social and economic outcomes of internet access. However, concerns remain over persistent first-level digital divide challenges including the affordability and reliability of these new services, particularly given high initial costs and lack of local technical support. We also note the trade-offs for local economies that accompany the adoption of such services, such as reliance on propretiary end-user terminals and highly centralized business operations. Future research will continue to track user experiences and the broader impact of LEO services in rural/remote Indigenous communities. • Low-Earth Orbital (LEO) satellites offer promise in addressing longstanding digital divides, including in Northern Canada. • Primary data and empirical research on user experiences with LEO satellites are limited. • We explore how end users in two remote fly-in Indigenous communities are adopting these technologies. • LEO satellites help address first-level (access, affordability, reliability) and third-level (social and economic outcomes) digital divides. • While LEO satellite services are promising, they result in trade-offs for end users and rural/remote communities.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".