Shaping the active, autonomous and responsible modern retiree: an analysis of discursive technologies and their links with neo-liberal political rationality
Bibliographic record
Abstract
Retirement is undergoing structural and discursive transformations that have implications for the individual and social experience and management of later life. Although discourses about retirement do not determine how individuals will prepare for and act as ‘retirees’, they provide morally-laden messages that shape people's possibilities for being and acting. Using Canadian newspaper articles published in 1999 and 2000, and drawing upon the governmentality perspective, this study explores the interconnections between neo-liberal political rationality and discursive constructions of ‘retiree’ subjectivities. The analysis demonstrates the ways in which certain subjectivities, and their associated technologies and practices of the self that are consistent with neo-liberal political rationality, are being shaped as ideal for ‘retirees’. The paper critically examines this process and its implications, and argues that the personal ‘freedom’ promised with the idealised life practices is ultimately illusory, because they oblige older people to resist or defy ageing through relentless projects of self-reflection and improvement, self-marketing, risk management, lifestyle maximisation and body optimisation. The implications of the neo-liberal discourse about old age and ‘retirees’ for future social policies and older people's services are critically examined.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.052 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".