The power(s) of observation: theoretical perspectives on surveillance technologies and older people
Bibliographic record
Abstract
There is a long history of surveillance of older adults in institutional settings and it is becoming an increasingly common feature of modern society. New surveillance technologies that include activity monitoring, and ubiquitous computing, which are described as ambient assisted living (AAL) are being developed to provide unobtrusive monitoring and support of activities of daily living and to extend the quality and length of time older people can live in their homes. However, concerns have been raised with how these kinds of technologies may affect user's privacy and autonomy. The objectives of this paper are 1) to describe the development of home-based surveillance technologies; 2) to examine how surveillance is being restructured with the use of this technology; and 3) to explore the potential outcomes associated with the adoption of AAL as a means of surveillance by drawing upon the theoretical work of Foucault and Goffman. The discussion suggests that future research needs to consider two key areas beyond the current discourse on technology and ageing, specifically: 1) how the new technology will encroach upon the private lived space of the individual, and 2) how it will affect formal and informal caring relationships. This is critical to ensure that the introduction of AAL does not contribute to the disempowerment of residents who receive this 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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.063 |
| Scholarly communication | 0.008 | 0.021 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".