In line or at odds with active ageing policies? Exploring patterns of retirement preferences in Europe
Bibliographic record
Abstract
ABSTRACT Faced with demographic ageing, European policy makers since the mid-1990s have taken a turn from fostering early retirement to promoting longer working life by reducing early exit incentives and facilitating work continuation. However, it remains open whether these reforms are yet reflected in the retirement plans and preferences of future pensioners’ cohorts. Using most recent data on desired retirement ages from the fifth wave of the European Social Survey (2010/11 wave), this paper empirically investigates how far current policy reforms are in line with the retirement age preferences of older workers aged 45 and over. Results show that older workers approaching retirement ages still intend to retire before the politically envisioned age of 65, and in many cases also before nationally defined standard retirement ages. Despite visible progress in implementing active ageing measures, the challenge of motivating older workers to continue working until or even beyond retirement ages thus remains. At the same time, there are regime-specific problem groups that face difficulties in adjusting to the active ageing paradigm of longer working life. Especially in countries with little employment support, those with unstable work careers, employment interruptions and few financial resources are at a high risk of being crowded out from late career employment and thus from the possibility of ensuring a decent standard of living in old age.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".