High-Poverty Nonmetropolitan Counties in America: Can Economic Development Help?
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
Despite significant poverty reductions in nonmetropolitan America during the 1990s, Census 2000 reports that hundreds of counties still possess high poverty rates. They have not only populations that are disproportionately minority, lack education, and live in single-parent households, but also weak job growth and low levels of labor force participation. To assess the potential antipoverty benefits of economic development in high-poverty counties, the authors compare their poverty-generating process with that of remaining nonmetropolitan counties. A primary finding is employment growth reduces poverty more in high-poverty counties. Likewise, completion of high school and obtaining an associate degree reduce poverty more in these counties. These patterns also hold for counties with persistently high poverty across decades. Thus, the authors are guardedly optimistic that high-poverty counties, even those where poverty has been persistent, will experience reduced poverty if economic development policies successfully stimulate job growth and increase human capital.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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