Appalachian Migration Patterns, 1975-1980 and 1985-1990
Bibliographic record
Abstract
following characteristics:! Appalachia experienced remarkable demographic change; in- and outmigration accounted for a turnover of over a quarter of the region’s population.! Appalachia has become an amenity region for students seeking higher education; over 100,000 more college-enrolled students came into the region than departed it.! Older Appalachians have been aging in place; relatively few persons 65 or older were newcomers to the region.! Appalachia has become more diverse; African-American and Hispanic populations increased through migration, especially in the Southern sub-region.! Appalachia has become poorer; migrants entering the region had lower-status jobs, lower incomes, less education, and were more likely to be living in poverty than those leaving the region.! Conditions vary widely among Appalachian sub-regions. Northern and Central Appalachia have been losing population while simultaneously becoming a destination for low-income, blue-collar migrants with little formal education. Southern Appalachia has been gaining population, and its inmigrants were more ethnically and racially diverse, better paid, more educated, and worked at higher status jobs than did migrants entering the other two sub-regions.! Appalachian migration patterns have changed from long-range flows into distant metropolitan areas to short-range exchanges principally centered around cities in and immediately adjacent to the region.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".