Ageing, embodiment and datafication: Dynamics of power in digital health and care technologies
Bibliographic record
Abstract
As a growing body of work has documented, digital technologies are central to the imagining of aging futures. In this study, we offer a critical, theoretical framework for exploring the dynamics of power related to the technological tracking, measuring, and managing of aging bodies at the heart of these imaginaries. Drawing on critical gerontology, feminist technoscience, sociology of the body, and socio-gerontechnology, we identify three dimensions of power relations where the designs, operations, scripts, and materialities of technological innovation implicate asymmetrical relationships of control and intervention: (1) aging bodies and the power of numbers, (2) aging spaces and the power of surveillance, and (3) age care economies and gendered power relations. While technological care for older individuals has been promoted as a cost-effective way to enhance independence, security, and health, we argue that such optimistic perspectives may obscure the realities of social inequality, agist bias, and exploitative gendered care labour.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.042 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".