Bibliographic record
Abstract
Social change in the 21st century is shaped by both demographic changes associated with ageing societies and significant technological change and development. Outlining the basic principles of a new academic field, Socio-gerontechnology, this book explores common conceptual, theoretical and methodological ideas that become visible in the critical scholarship on ageing and technology at the intersection of Age Studies and Science and Technology Studies (STS). Comprised of 15 original chapters, three commentaries and an afterword, the book explores how ageing and technology are already interconnected and constantly being intertwined in Western societies. Topics addressed cover a broad variety of socio-material domains including care robots, the use of social media, ageing in place technologies, the performativity of user involvement and public consultations, dementia care and many others. Together, they provide a unique understanding of ageing and technology from a social sciences and humanities perspective and contribute to the development of new ontologies, methodologies and theories that might serve as both critique of, and inspiration for, policy and design. International in scope, including contributions from the UK, Canada, USA, Germany, Norway, Denmark, Austria, The Netherlands, Spain and Sweden, Socio-gerontechnology is an agenda-setting text that will provide an introduction for students and early career researchers as well as more established scholars that are interested in ageing and technology.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".