Detection of drought-related human migration and population change on the North American Great Plains
Bibliographic record
Abstract
Rural migration responses to drought are complex, context specific, and multi-directional. Migration is one of many possible adaptive responses to drought, and is typically initiated only after other, less disruptive strategies have been attempted. The potential for drought to stimulate migration or displacement is inversely related to the range of alternative adaptation options available to households, and is lowered through coordinated vulnerability-reduction mechanisms such as institutional water-management regimes and crop insurance programs. When drought-related migration does occur, it tends to flow along pre-existing social networks to known destinations, which are usually urban centres within the same state/country or in contiguous ones. Using a mixed-methods approach that combines geospatial tools, quantitative methods (i.e. random forest and spatial regression) and qualitative data gathered through archival research and local interviews, we have generated detailed models of the changing influence over time of drought on rural population patterns on the North American Great Plains. In this presentation we highlight key findings from our work, describe data needs and limitations, discuss the predictive power of various quantitative methods, identify non-climatic variables that mediate migration outcomes, and emphasize the importance of mixed-methods approaches.
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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 | 0.002 |
| 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 | 0.001 |
| 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".