SILVER TSUNAMIS, GRAY TIDES, AND DEAD WOOD: EXPLORING AGEIST METAPHORS AT THE INTERSECTION OF AGING AND CLIMATE CHANGE
Bibliographic record
Abstract
Abstract This paper explores the ageist metaphorical language that links demographic change and climate change from a cultural gerontological perspective. Older adults are often labeled as ‘vulnerable’ and ‘at risk’ in the face of the climate crisis, framing it as a costly public health challenge. Simultaneously, they face criticism and animosity, being perceived as contributors to environmental degradation. The metaphorical realms of aging and climate change converge in ageist metaphors such as ‘the silver tsunami’ or ‘the gray flood’ that reinforce the image of the double burden allegedly represented by older people. Canadian sociologist Stephen Katz labeled this perspective “alarmist demography,” challenging the notion that the threat arises from a growing, demanding, and relatively affluent group relying on the welfare state, burdening younger generations (1992, 203). Scrutinizing the rising ageism in climate change discourse, this paper illustrates how ageist metaphors might act as a veil, concealing other socio-political issues that warrant attention, such as social inequalities. To showcase this phenomenon, we will use examples from media and film as well as Margaret Atwood’s short story “Torching the Dusties” (2014), which depicts young people setting nursing homes on fire, burning down “the dead wood” to force the old to “make room” for the young. Using such recent cultural representations, we will make the dangerous ageism inherent in the double burden narrative very explicit. To conclude, the paper shows that generational warfare is not helpful, and that tackling climate protection requires a collective effort involving all generations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.029 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".