Embodiment and dementia: Exploring critical narratives of selfhood, surveillance, and dementia care
Bibliographic record
Abstract
In the last decade there has been a notable increase in efforts to expand understandings of dementia by incorporating the body and theorizing its interrelationship with the larger social order. This emerging subfield of dementia studies puts the body and embodied practices at the center of explorations of how dementia is represented and/or experienced. This shift towards a greater recognition of the way that humans are embodied has expanded the horizon of dementia studies, providing the intellectual and narrative resources to examine experiences of dementia, and their interconnections with history, culture, power, and discourse. Our aim in this paper is to critically explore and review dimensions of this expanding research and literature, specifically in relation to three key narratives: (1) rethinking selfhood: exploring embodied dimensions; (2) surveillance, discipline, and the body in dementia and dementia care; and (3) embodied innovations in dementia care practice. We argue that this literature collectively destabilizes dementia as a taken-for-granted category and has generated critical texts on the interrelationship between the body and social and political processes in the production and expression of dementia.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| 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".