A tale of two homecomings: the fragmented reintegration of first- and 1.5-generation returnees in Mexico
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract During the past decade, an unprecedented number of Mexicans residing in the U.S. returned to Mexico. This high level of return migration entails great social, cultural, economic, and political challenges for a country that has long struggled to absorb its young and low-skilled workforce—and that is likely to continue receiving returnees as immigration enforcement in the United States intensifies. Drawing on semi-structured interviews with Mexican migrants returning from the United States, this paper examines the key barriers that returnees face to be able to reintegrate successfully. The analysis shows that first-generation migrants or those who migrated to the United States in adulthood and 1.5-generation migrants or those who migrated as minors with their parents have strikingly different reintegration experiences upon return. Findings also point to a fragmented form of embeddedness, in which returnees may reintegrate along one dimension (social, economic, or psychological) but not others. These insights contribute to the literature on return migration by deepening our understanding of the complexities of reintegration in the Mexican context, with the aim of informing more effective policy responses and anticipating future challenges.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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 it