EXPLORING THE RELATIONSHIP BETWEEN DROUGHT AND POPULATION CHANGE ON THE NORTH AMERICAN GREAT PLAINS, 1970-2010
Bibliographic record
Abstract
Through the second half of the 20th century, the North American Great Plains saw widespread rural out-migration, a continuation of trends that began with the Dust Bowl crisis during the Great Depression of the 1930s. As part of a wider academic focus on the roles climate and environmental changes have on migration, this research project sought to understand the relationship between drought conditions and rural population decline on the Great Plains. In this explorative research, census population data for Canada and the US from 1970-2010 were analyzed along with temperature, precipitation, and Palmer Drought Severity Index data for the same period using a variety of regression to seek out possible association between drought conditions and population loss at local scales. As part of this process, a novel index for identifying drought likelihood was also developed and tested. Results indicate that the significance and direction of the relationship between drought and rural population loss is spatially heterogenous. Geographically weighted regression models are demonstrated to have better predictive power than traditional regression methods, although that predictive power deteriorates through the decades in the study period. Small clusters of counties were detected where the drought-population loss is relatively strong in certain decades, but generally the results suggest that non-climatic factors were the primary drivers of population loss across the Great Plains. The modelling results are discussed in the context of a case study of Lincoln County, Colorado, a dryland county visited as part of field research for this project.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".