Conclusion: Precarity and Ageing in the 21st Century
Bibliographic record
Abstract
Introduction This book has explored the extent to which the concepts of ‘precarity’ and ‘precariousness’ can provide new insights into the way in which inequalities and insecurities may be experienced across the life course. A dominant theme of the chapters has been the impact of various changes that have affected the lives of older people in the 21st century, these including the decline of the social or welfare state, the move to extend working life, the pressures faced by caregivers and the vulnerabilities of late old age. Of course, as many of the contributors suggest, not all older people are affected in the same way, with significant variations according to race, ethnicity, gender, social class and sexual orientation. But relatively few people are likely to escape the experience or feeling of insecurity at some point in later life, and for many these may become deeply rooted in everyday life. As a general argument, then, and for different reasons, we think ideas associated with ‘precarity’ and ‘precariousness’ are valuable for students of ageing and policymakers to consider. First, they extend our understanding of the many sources and forms of insecurity that may affect the lives of older people. These have been illustrated in this book through, for example, discussions relating to migration (Kobayashi and Kahn), care work (Fine), cuts to health and social care (Polivka and Bao; Portacolone), and the influence of life course transitions in creating precarity and responses to it (Settersten). Second, later life may bring distinctive features in respect of changes to mental and physical health. As noted in many of the chapters, however, these are often best understood in terms of lives that, from childhood onwards, may face numerous threats to independence and security (see, for example, the chapters by Settersten and Grenier). Third, on an optimistic note, the idea of precarity may also be used to point the way to new forms of resistance developed by individual older people and by larger social groups (an aspect identified in the chapters by Katz and Phillipson). Thus, the debate about precariousness is not only about understanding new vulnerabilities but also about the emergence of collective organizations in response, both across and within generations (see later in this chapter).
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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 | 0.005 |
| 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 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".