Rural America at a glance
Bibliographic record
Abstract
The U.S. rural population is growing again after a decade of overall population loss, with growth of approximately a quarter percent from 2020 to 2022. This growth occurred because rural in-migration was larger than declines in the natural rate (the number of births compared with the number of deaths) of population growth. The rural population is also experiencing declines in poverty. In 2021, 9.7 percent fewer nonmetropolitan counties experienced persistent poverty (20 percent or more of the population had poverty level household incomes in each of the last four decennial Census years) compared with a decade earlier. Still, more than half of extremely low-income nonmetropolitan renter households experienced housing insecurity. This issue was particularly acute for American Indian or Alaska Native and Hispanic households. This report examines recent issues such as rural population and migration trends, poverty, housing insecurity, employment, and clean energy jobs. The report finds that rural employment levels and annual growth rates nearly returned to those seen in the years prior to the Coronavirus (COVID-19) pandemic. Finally, highlighting an emerging employment area of interest, approximately 1 percent of nonmetropolitan workers hold clean energy jobs
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.175 | 0.055 |
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".