“With Friends Like These”: Unpacking Panicked Metaphors for Population Ageing
Bibliographic record
Abstract
Age studies scholars have long noted problems with using a tsunami metaphor to describe population ageing. Age-friendly offers a new way to respond to an increase in older adults. Though critical gerontologists identify the related movement’s limits, “age-friendly” itself is rarely recognized as a metaphor. This paper proposes that, while the metaphor of age-friendly is more benign than that of the tsunami, it still portrays an ageing population as a homogenous problem to be solved through morally obligatory individual actions, thereby participating in a form of age panic. The analysis draws on a humanities-based close reading of the World Health Organization’s 2007 “Global Age-Friendly Cities: A Guide.” The method uncovers attitudes that anchor the metaphor and hamper the movement’s effectiveness, particularly when trying to reach people who have not already been well served all their lives. The emphasis on a narrow version of active ageing feeds a neoliberal imagination that affects how value is assigned to an ageing population. That underlying emphasis needs to shift before new metaphors, policies and practices for population ageing—that allow for the variability and uniqueness of late-life experience—can take hold. How might we reconceptualize the ageing population if we focus on contributions and meaning instead?
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.051 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".