Bibliographic record
Abstract
Many people emigrating abroad eventually return home. Yet, little is known about the returnees: who are they and how do they compare to those who did not return? How does their decision to return depend on economic situation at home? In this paper, I empirically analyze the propensity of US immigrants to return. To identify return migration, I use the method adopted from Van Hook et.al. (2006). The method is based the U.S. Current Population Survey (CPS) which\ninterviews households for two consecutive years. About a quarter of foreign-born individuals drop out of the sample between the first and the second years, due to various causes including return migration.\nAfter eliminating all other causes of dropout, I estimate the propensity of immigrants to return, depending on personal and home country characteristics. I find that the difference between recent immigrants and other immigrants is greater than the difference between men and women, or skilled and unskilled migrants. Thus, assimilation differentiates immigrants more in their decision to return than education or gender. In particular, distance to home country negatively affects return propensity of those who arrived over 10 years ago, and has no effect on recent immigrants.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".