Building a digital republic to reduce health disparities and improve population health in the United States
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Income, schooling, and healthcare are key ingredients for optimizing human’s ecological niche for survival. But most government programs that are designed to provide a hand up in these domains are difficult to access. While many Americans struggle to pay taxes, few understand the difficulties associated with enrolling in Medicaid, Temporary Assistance for Needy Families. A remarkably small percentage of needy families receive the social benefits to which they are entitled, and that percentage is smaller for those most in need (those with physical disabilities, caregiving responsibilities). To address this problem, the Child Tax Credit in the American Rescue Plan provided automatic enrollment, and worked hard to locate more low-income families. But until everyone has a digital footprint that allows automated enrollment, the sickest and most vulnerable citizens will remain in the informal sector. By expanding data systems so that all Americans have a digital identity across multiple datasets, it not only becomes possible for all Americans to simplify their lives but for welfare services to work for the most vulnerable, as they are intended.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it