Bibliographic record
Abstract
Introduction Scholarship over the last 40 years has explored the contexts in which retirement migration unfolds, how these have changed over time and the diversity in retirement migrants’ motivations and experiences. We depict increasing precarity as a social fact resulting from neoliberalism and austerity trends in the Global North (Phillipson, 2021), one that results in anxiety about the future (Lain et al, 2019, 2021; Grenier et al, 2021). To some extent, such precarity has been related to ageism, whether in terms of discrimination in the labour market or welfare state policies (see Phillipson, 2021). Often overlooked, however, is that ageism (and the specific impacts it has on precarity) also influences older people's plans and experiences, including those of international retirement migration. In this chapter, we discuss the importance of examining how ageism influences precarity and the experiences of retirement migrants. We point to global inequalities borne of colonialism, wherein accumulation was centred in countries of the Global North, many of which occupied, dominated and exploited the people and natural resources of countries in the Global South. In the present era of unequal global relations, the ‘unequal accrual of privileges and rewards from a global system of production and accumulation’ (Hayes, 2018a: 11) undergird retirement migrants’ statuses and options, as well as shape their interactions with local populations. We end the chapter with a description of our study, which took place in Spain, Costa Rica and Mexico, and of our sample, which was composed of Swiss, British and US retirement migrants. Retirement migration: what do we know? Retirement migration has drawn attention from scholars in North America since the early 1980s, as it has become a more frequent practice (Calzada and Gavanas, 2018). Some early studies focused on seasonal migration to the US Sunbelt – that is, the movement of North American retirees (US and Canadian citizens) to the warmer regions of the US for the winter. Longino and Biggar (1981) conducted some of the first research on seasonal and permanent North American retirement migrants’ social ties and interactions in the US Sunbelt. They showed that the retirees who migrated permanently to these areas fostered the integration of those who came only for a couple of months every year and that, together, the two groups constructed communities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.248 | 0.079 |
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".