Using alternative data sources to study rural migration: examples from Illinois
Bibliographic record
Abstract
Abstract One of the problems frequently faced by migration researchers is the paucity of national census migration data for small rural areas. Drawing upon a larger, multi‐level research project that focused upon rural poverty migration within Illinois, this article illustrates the opportunities and challenges associated with using community‐based data sources to measure mobility in small rural communities. Use of alternative migration data sources can present opportunities to circumvent common problems with census‐based data sources, including addressing temporal shortfalls, providing custom geographies, and capturing the movement of difficult‐to‐enumerate subpopulations. Alternative data‐sets may also allow researchers to formulate new questions that are more oriented to society's pressing problems. Yet, users of non‐traditional data‐sets also face issues of indirect measurement, data quality, comparability, replication, costs, and moral and ethical concerns. As use of non‐traditional migration data sources increases, researchers will need to become familiar with both the benefits and pitfalls of these data‐sets. Copyright © 2008 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".