Bibliographic record
Abstract
Abstract This paper examines the distribution of the older beneficiaries of state social security programmes, with particular attention to the UK, the US and Australia. These data have not previously been analysed by a migration researcher, provide a partial source on international retirement moves, and make clear that several processes and types contribute to the international dispersal of a country's retirement population. They show that ‘return’ and ‘family‐joining’ migrations are the predominant form, and that their forms and destinations are changing. The British increasingly select European destinations at the expense of the formerly dominant destinations of Australia, Canada, New Zealand and South Africa. The US continues to be a leading destination for British and German retirees. European data also show that ‘amenity‐seeking’ retirement moves from northern to Mediterranean countries have increased rapidly in recent decades and are growing faster than other types of retirement migration. Social security records also reveal new patterns of return migrations – the number of British pensioners in several Caribbean countries is increasing rapidly, and high shares of Australian overseas migrants are in Mediterranean countries such as Italy, Greece and Malta. Given the paucity of research on these emergent migration flows, the paper concludes by discussing both the prospects for their growth and research priorities. Copyright © 2001 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".