Bibliographic record
Abstract
Abstract There are massive bodies of critical sociological research focused on climate change or on aging, but less frequently on both at once. Understanding the intersecting dynamics of climate change and aging is a complex task that overlaps with the rapidly emerging field of climate gerontology. This entry introduces key themes within recent aging and climate change scholarship, and charts potential areas for further research, around four themes: uneven vulnerabilities to climate change; support for meaningful adaptation; (inter)generational grief and responsibility; and advocacy/activisms. The entry concludes that sociologists can play a role in better understanding which groups of older adults are more at risk, and what underpins their vulnerabilities. By undertaking critical research that centers the lived experiences of the most vulnerable and marginalized older adults, they can also inform equitable adaptation strategies. In addition, future sociological research could offer critical analyses of how age intersects with multiple systems of oppression within climate justice movements, and how intergenerationality occurs in different contexts. Finally, sociologists can play an important role in exposing underlying ageism and ableism in all aspects of climate change discourse, policy, and research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.004 |
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".