Inferring Fine-grained Migration Patterns across the United States
Bibliographic record
Abstract
Fine-grained migration data illuminate important demographic, environmental, and health phenomena.However, migration datasets within the United States remain lacking: publicly available Census data are neither spatially nor temporally granular, and proprietary data have higher resolution but demographic and other biases.To address these limitations, we develop a scalable iterative-proportional-fitting based method that reconciles high-resolution but biased proprietary data with low-resolution but more reliable Census data.We apply this method to produce MIGRATE, a dataset of annual migration matrices from 2010-2019 that captures flows between 47.4 billion pairs of Census Block Groups -about four thousand times more granular than publicly available data.These estimates are highly correlated with external ground-truth datasets, and improve accuracy and reduce bias relative to raw proprietary data.We use MIGRATE to analyze both national and local migration patterns.Nationally, we document temporal and demographic variation in homophily, upward mobility, and moving distance: for example, we find that people are increasingly likely to move to top-income-quartile CBGs and identify racial disparities in upward mobility.We also show that MIGRATE can illuminate important local migration patterns, including out-migration in response to California wildfires, that are invisible in coarser previous datasets.We publicly release MIGRATE to provide a resource for migration research in the social, environmental, and health sciences.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".