Refugees From Dust and Shrinking Land: Tracking the Dust Bowl Migrants
Bibliographic record
Abstract
We construct longitudinal data from the U.S. Census records to study migration patterns of those affected by the Dust Bowl of the 1930s.Our focus is on the famous "Okie" migration of the Southern Great Plains.We find that migration rates were much higher in the Dust Bowl than elsewhere in the U.S.This difference is due to the fact that individuals who were typically unlikely to move (e.g., those with young children, those living in their birth state) were equally likely to move in the Dust Bowl.While this result of elevated mobility conforms to long-standing perceptions of the Dust Bowl, our other principal findings contradict conventional wisdom.First, relative to other occupations, farmers in the Dust Bowl were the least likely to move; this relationship between mobility and occupation was unique to that region.Second, out-migration rates from the Dust Bowl region were only slightly higher than they were in the 1920s.Hence, the depopulation of the Dust Bowl was due largely to a sharp drop in migration inflows.Dust Bowl migrants were no more likely to move to California than migrants from other parts of the U.S., or those from the same region ten years prior.In this sense, the westward push from the Dust Bowl to California was unexceptional.Finally, migration from the Dust Bowl was not associated with long-lasting negative labor market effects, and for farmers, the effects were positive.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".