Population responses to the 1976 South Dakota drought: Insights for wider drought migration research
Bibliographic record
Abstract
Abstract Droughts on the North American Great Plains once led to elevated levels of out‐migration from rural areas. Large‐scale drought migration has not been observed since the 1950s due to changes in land management and agricultural systems that lessened farm‐level vulnerability to drought. Have droughts had less observable population impacts in subsequent decades? Here, we present findings from an investigation of an unusually severe, localised drought that struck eastern South Dakota in 1976 and caused staggering financial losses to farms. County‐level population and net migration rates show an anomalous increase of migration into drought‐affected counties by male migrants in the age group 30–35 years, likely being return migrants coming to help on the family farm. Newspaper archives and interviews with retired farmers suggest that few people moved away during the 1976 drought; most adapted instead by selling off their livestock herds and taking on greater debt. However, a commonly expressed view is that the drought ‘softened up’ area farmers, increasing their vulnerability to interest rates that quadrupled in the three following years. The early 1980s saw high rates of farm failures, unemployment and population decline in counties that experienced the worst impacts of the 1976 drought, suggesting the drought had a lag effect on population patterns. The findings from this case study are consistent with the ‘lessening hypothesis’ that social and technological innovations reduce economic and population impacts of recurrent climatic risks but elevate vulnerability to less frequent, unusually severe events.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".