The Territorial and Socio-Economic Characteristics of the Digital Divide in Canada
Bibliographic record
Abstract
The digital divide in Canada has gained significant attention from policymakers and the public in 2020 as a result of the COVID-19 pandemic. The pandemic enhances the vulnerability of residents in rural and Indigenous communities that lack high-speed Internet access which affects their residents’ ability to participate in an online work and learning environment. However, digital inequalities also remain an issue in urban settings despite the physical infrastructure that is usually in place to connect to high-speed Internet. The federal government has launched several funding initiatives at the end of 2020; however, this paper argues that the current federal policy strategy to address the digital divide is insufficient. By drawing on the intersectional character of the digital divide, which is interlinked with other types of socio-economic inequalities, this paper investigates why the federal broadband development approach remains problematic. As the digital divide in Canada persists, this paper explores current federal funding initiatives and their effectiveness in supporting broadband deployment across rural and Indigenous communities. The analysis shows inequalities regarding broadband access and funding distribution in Canada which also stem from a lack of democratic efficacy during federal hearings.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".