National Health Insurance: Truly Taking Care of a Nation
Bibliographic record
Abstract
Implementing national (universal) health insurance for the United States is an issue that deserves to be looked at by government officials. Having this type of insurance coverage in the country would help to cover the millions of uninsured and underinsured citizens of the United States. In order to support the reasons for implementing the universal coverage, information has been gathered that covers a variety of areas. For over a century, the American government has discussed the issue of national health insurance. Past supporters of the issue include former Presidents Truman and Nixon. Recently, Ezekiel Emanuel and Anna M. Miller have created types of universal coverage systems that can easily been implemented without causing too much frenzy for citizens. To help show people how universal coverage affects citizens, there is information included about the plans that have been implemented by San Francisco and Canada. San Francisco has created, implemented, and expanded its Healthy Kids Program. This program covers children and young adults until the age of 24. On a national level, Canada has implemented universal coverage for its citizens and it has had a positive effect on the country, the citizens, the government, the economy, and the healthcare industry.
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.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".