Enabling Universal Connectivity via Data-Driven Policymaking: A North American Case Study
Bibliographic record
Abstract
The lack of universal broadband connectivity is becoming a significant hurdle in today's growing digital world. Despite considerable efforts via numerous grant programs, subsidies, private investments, and research initiatives, the disparity between urban and rural communities continues to widen. The COVID-19 pandemic has further emphasized the importance of affordable high-speed Internet to access many social and economic opportunities including remote work, distance learning, and telemedicine. Nevertheless, with the rise of big data, it is now possible to better understand this digital divide from a data-driven perspective. In this article, we leverage a massive crowd-sourced dataset of smartphone device measurements to extract key insights regarding the current state of the digital divide in North America. Specifically, we quantify the extent of the gap, examine the impact of the COVID-19 pandemic, and explore possible socioeconomic factors that may be influential in operator business models. Finally, we demonstrate how crowdsourced datasets can be leveraged to monitor the impact of emerging technologies, to better direct federal broadband funds, and to make more informed spectrum policy decisions.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".