Bibliographic record
Abstract
• The number of uninsured nonelderly adults fell by an estimated 10.6 million between September 2013 and September 2014 as the uninsurance rate fell from 17.7 percent to 12.4 percent—a drop of 30.1 percent. • Most of the gain in coverage was among the low- and middle-income adults targeted by the ACA's Medicaid and Marketplace provisions. • The uninsurance rate dropped 36.3 percent in states that implemented the ACA’s Medicaid expansion, compared with 23.9 percent in nonexpansion states; 54.7 percent of uninsured nonelderly adults lived in nonexpansion states in September 2014. The Urban Institute’s Health Reform Monitoring Survey (HRMS) has been tracking insurance coverage since the first quarter of 2013. Data from the HRMS have provided an early look at changes in the nation’s uninsurance rate following the implementation of the Affordable Care Act’s (ACA) key coverage expansion provisions, including the launch of new health insurance Marketplaces and the state option to expand Medicaid to nearly all adults with family income at or below 138 percent of the federal poverty level (FPL).1 The HRMS provides early feedback on ACA implementation to complement the more robust assessments that will be possible when the federal surveys, which are
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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.374 | 0.278 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".