Co-designing a community-led Internet assessment tool in Rigolet, Nunatsiavut, Canada
Bibliographic record
Abstract
Inequitable access to telecommunication networks is having discernible impacts on Canadians, particularly in Northern and remote communities. More complete, available, and interoperable datasets are needed to better quantify inequitable access, particularly at the ‘last mile’ of the internet. Community-based internet assessment initiatives have been recognized as valuable programs in this pursuit. The Rigolet Internet Assessment Initiative (RIAI) is a project located in and led by the Inuit of Rigolet, Nunatsiavut, to measure and better understand the community’s telecommunications environment. The RIAI data collection began in 2019 and involves collecting measurements every twenty minutes from ten participating households. Using participatory design methods, this paper describes the co-creation of the initiative, the design of the system and development of the software suite, deployment of the integrated hardware tools, and analysis of a sample of the resulting dataset. As a result of this work, an additional 9,436 measurements were added to databases of publicly available download and upload speeds for the community of Rigolet (where only 2 existed prior) where they can be used to support data-driven policy. Further, findings indicate that the quality of internet in Rigolet is far below the Canadian Radio and Telecommunications Commission’s upload and download speed goals.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".