Returning Home and Making a Living: Employment Strategies of Return Migrants to Rural U.S. Communities
Bibliographic record
Abstract
This research focuses on return migration to rural areas in the United States and documents strategies that return migrants use for securing employment. Rural labor markets, due to their small size, limited diversity, and lower wage scale, can be challenging for people looking to make a living. To understand how these labor market constraints affect rural return migration, we draw on over 300 semi-structured interviews with stayers, outmigrants and return migrants. Conversations, conducted at 10- to 30-year high school reunions in geographically isolated rural U.S. communities, affirm the well-known challenges and significant barriers to employment in small towns. However, additional interviews with community and business leaders also document employers' difficulties in filling skilled work positions. Return migrants take on jobs both in the public and private sector, but quite a few carve niches through self-employment, mostly in service sectors. We also encountered a small number of return migrants who started internet-based businesses or otherwise worked remotely. A reoccurring theme highlights how return migrants accept career sacrifices in order to raise their children in a familiar, small-town environment. We conclude that return migrants can be a boost to the economic and social vitality of rural communities and that communities should make efforts to both attract and retain them. Keywords: return migration, rural communities, rural labor markets, employment, geographic isolation, United States
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.000 |
| 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".