Governing the ageing body: explicating the negotiation of ‘positive’ ageing in daily life
Bibliographic record
Abstract
ABSTRACT Positive ageing discourses have proliferated in Western nations, forming key aspects of structured mandates for how to think about, and act towards, ageing bodies. As interpretive resources, positive ageing discourses shape how adults growing older think about themselves, their bodies and the bodies of others in relation to the process of ageing and the imperative to ‘age well’. Informed by governmentality, this paper considers how positive ageing discourses function as technologies of government to inform and direct conduct. Drawing on in-depth narrative data, this analysis traces how ageing citizens take up and negotiate positive ageing discourses in their everyday lives, drawing attention to the intensive work, inexorable focus on the body and numerous resources that the enactment of positive ageing requires. Specifically, this analysis illuminates the interplay between the lived experiences of ageing and the socio-culturally structured mandates that shape how ageing and ageing bodies are conceptualised and approached, and draws attention to the moments of tension that arise out of such interplay. We suggest that these moments of tension highlight how the bodywork practices that older adults rigorously and continuously engage in are not so much directed towards the pursuit of ageless ageing, but rather are a response to the inescapable threat of dependency, decline and loss of agency, and thus operate to affirm ageist underpinnings of positive ageing discourses.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.053 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".