MIGRATION OF U.S. AND CANADA RETIREES TO LATIN AMERICAN COLONIAL CITIES: LESSONS LEARNED
Bibliographic record
Abstract
International retirement migration will accelerate with the aging of the baby boomer generation. In the Western hemisphere, many migrants favor medium-sized, historic, picturesque Latin American colonial cities. Much is known about the motivation and activities of the immigrants, but their impact on the host cities has received little study. To better understand this issue, we conducted 79 interviews in Spanish with a stratified sample of local residents in two historic colonial cities that have been targets of significant retirement migration from the US, Canada, and Europe: Cuenca, Ecuador, and San Miguel de Allende, Mexico (SMA). In both cities we interviewed individuals from six categories: government officials, health care providers, real estate agents, human services providers, and convenience store (tienda) owners in high and low retiree areas. Interview data were compared and contrasted with results of online surveys of 400 retired immigrants in Cuenca and 297 in SMA. Although interviewees generally felt that retiree immigration was good for the city, they tended to feel that migration had increased the cost of living and created a need for locals involved in business to learn English. Nonetheless, general feelings toward the retired immigrants were favorable. Retirees were felt to be friendly and open, and to respect the local culture. However, respondents strongly felt that persons who moved there should learn Spanish, which was not surprising considering that 75% of retirees surveyed rated their Spanish language skills as absent, limited, or confined to simple conversation. Suggestions for immigrants and local residents will be discussed.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".