Barriers to Meaningful Connectivity
Bibliographic record
Abstract
Community networks risk failure when they attempt to emulate models from elsewhere without engaging the community in the process and making appropriate adaptations. These ‘build it and they will come’ models rarely work over the long term. This research project explored claims from residents of a low-income neighbourhood in the “North End” of Winnipeg in Manitoba, Canada, that inadequate and unaffordable Internet connectivity limits their access to critical communication tools, resources, and information. Through the research, we identified the need for a sustainable model of affordable, accessible Internet connectivity that centers on building a cooperative-owned and operated community network with Indigenous and newcomer families at its heart. Findings revealed that high connectivity costs, limited digital literacy, and inadequate infrastructure are the primary barriers to meaningful connectivity in the community. The intent of the “North End Connect” research project was to work directly with the residents, to learn about their connectivity needs and wants, inform the project’s technical team as to how and where to build a solution that works for the community, removing explicit and implicit barriers to access. Through our research, we validated that digital connectivity is a problem in the community. Utilizing a CBPAR approach provided a more nuanced understanding of the barriers to access from the resident’s perspective and lived experience. This allowed for the development of a strengths-based roadmap that utilized existing assets to provide affordable, accessible, trustworthy, and secure Internet access to anyone who wants it. The research acted as the catalyst to motivate the community and led to ongoing interventions aimed at addressing each of the identified barriers. As we investigate these barriers, it becomes evident that addressing these issues is not just a matter of technological access but a crucial step toward fostering a more inclusive and equitable society. The project serves as a model for community-driven digital inclusion efforts and contributes to global conversations about equitable access to the internet.
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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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.031 | 0.002 |
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".