Measuring the effectiveness of digital inclusion approaches
Bibliographic record
Abstract
Expanding access to quality, affordable broadband is an urgent national priority and billions of dollars in new investments are in the pipeline for infrastructure deployment and adoption, including $65B in the infrastructure bill recently passed by the U.S. Senate and pending before the House of Representatives. The literature review to date reveals that despite many existing and new initiatives at the federal, state, and local level, over 76 million Americans remained unconnected or underconnected (connected through a smartphone data plan only) in the first quarter of 2021, most of whom lived in low-income households. This study aims to analyze existing broadband affordability programs and to propose recommendations about how best to connect low-income households sustainably to high-speed Internet services they can afford and use for today’s online activities. To meet the research goal, this study will apply a mixed methods framework to identify and analyze case studies that illustrate best practices and challenges. The cost-effectiveness and efficiency of the programs will be evaluated, and representative stakeholders of these programs will be interviewed.
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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 | 0.048 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".