Bibliographic record
Abstract
For people in urban America, rural life can be hard to imagine. What is a quick walk around the block for milk in New York City can be a 15-mile drive in many parts of the country. A short ride to the doctor’s office in Los Angeles can be a round trip that takes an entire day for people in small towns, assuming that they have a car—and a doctor. A W.K. Kellogg Foundation study revealed that misconceptions and contradictory views about rural America are widespread: [R]ural life represents traditional American values but is behind the times; rural life is more relaxed and slower than city life, but harder and more grueling; rural life is friendly, but intolerant of outsiders and differences; and rural life is richer in community life, but epitomized by individuals struggling independently to make ends meet.1 Beyond the clichés is a rural America that is unique, diverse, com-plex—and too often fraught with health disparities. Of 41 million unin-sured Americans, about 20 percent are rural residents. Nearly one-half of rural residents suffer from a major chronic illness, yet rural residents average fewer medical appointments than people in urban areas.5,6 Health care provider shortages in rural areas extend to most medical disciplines, including dentistry.7,8,9 Even when services are available, people face distance- and time-related barriers to accessing care. Visit us online at www.hrsa.gov Only 10 percent of physicians practice in rural America, although 22 percent of Americans live there.2 Rural people living with HIV/AIDS are less likely than their urban counter parts to receive highly active antiretroviral therapy, or HAART, and 66 percent of rural residents on HAART travel to urban areas to receive care.3 Rural Americans are more likely than non-rural Americans to live below the Federal Poverty Level.4
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.106 | 0.018 |
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".