A GLOBAL AGETECH AGENDA
Bibliographic record
Abstract
Abstract AgeTech is increasingly seen as a way to support and enhance the health and independence of older people. However, AgeTech research and innovation agendas overwhelmingly focus on the needs of older people in developed countries and have so far failed to recognise that population aging is a global-level trend that intersects with other “megatrends”, such as climate change, urbanisation and international migration. Drawing on an environmental scan of the current and emerging AgeTech sector, this paper argues that technology research, development and innovation needs a global agenda that engages with initiatives such as the UN’s Strategic Development Goals and Decade for Healthy Ageing. It is also important to situate AgeTech in the wider debate on global disparities. The ongoing impact of colonial legacy (afterlife colonization) continues to reduce life chances, leads to poor health, and is present in everyday relationships between people and institutions at global and local levels. This is particularly apparent in the unequal impact of climate change on people and places, but as yet, the discussion of these issues is notably absent in the narratives surrounding AgeTech. This misses significant opportunities for global reciprocity in health, co-development of useful and appropriate technologies, and more sustainable approaches to innovation (e.g opportunities for frugal innovation), The paper highlights the potential role of older people as active participants in technological change, for example for mitigating climate change and its unequal impact on the Global South
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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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.014 | 0.023 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.017 | 0.011 |
| Insufficient payload (model declined to judge) | 0.039 | 0.009 |
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".