Digitization of Aging-in-Place: An International Comparison of the Value-Framing of New Technologies
Bibliographic record
Abstract
Planning for aging populations has been a growing concern for policy makers across the globe. Integral to strategies for promoting healthy aging are initiatives for ‘aging in place’, linked to services and care that allow older people to remain in their homes and communities. Technological innovations—and especially the development of digital technologies—are increasingly presented as potentially important in helping to support these initiatives. In this study, we employed qualitative document analysis to examine and compare the discursive framing of technology in aging-in-place policy documents collected in three countries: The Netherlands, Spain, and Canada. We focus on the framing of technological interventions in relation to values such as quality of life, autonomy/independence, risk management, social inclusion, ‘active aging’, sustainability/efficiency of health care delivery, support for caregivers, and older peoples’ rights. The findings suggest that although all three countries reflected common understandings of the challenges of aging populations, the desirability of supporting aging in place, and the appropriateness of digital technologies in supporting the latter, different value-framings were apparent. We argue that attention to making these values explicit is important to understanding the role of social policies in imagining aging futures and the presumed role of technological innovation in their enactment.
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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".