The securitisation of dementia: socialities of securitisation on secure dementia care units
Bibliographic record
Abstract
Abstract Nearly 50 million people around the world live with dementia, with statistics predicting a steady increase in prevalence for the foreseeable future. There is a need for comprehensive and compassionate dementia care. Long-term care homes have built special care units for people living with middle- to late-stage dementia. Among other services, these care units often use innovative security technologies that monitor and curtail movement beyond unit exit doors. As care-givers and technology developers grapple with the ethical dilemma of autonomy and risk management, researchers are beginning to investigate the social impact of these security technologies. The present research contributes to this line of inquiry. Fieldwork was carried out on two secure long-term care units for people living with dementia. Ethnographic accounts will illustrate how security technology creates socialities of securitisation on a secure dementia unit. Using securitisation theory, I will argue that dementia has been redefined, shifting it from a health issue to a security issue. The discursive construction of dementia as a security issue will be considered in terms of the co-constructed notions of vulnerability, risk, security threat and security challenge with respect to people living with dementia. The paper investigates how securitisation influences the ethics of dementia care.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.057 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".