Robots and care of the ageing self: An emerging economy of loneliness
Bibliographic record
Abstract
What happens when caring for the ageing population is so devalued that robots are deployed to care for our elders? We examine the growing employment of companion robots in elder care as one response to a critical labour shortage and loneliness epidemic shared across the Global North. Reflecting on interviews conducted with robot engineers, researchers, NGO care providers and local government, we examine five robots under development or in use in the UK and the USA. We ask if machines providing emotional and social care signal a diminishment of what it means to be human or if robots and automation present a promising solution to our elder care crisis. We do not evaluate the efficacy of robotic technology but identify and question assumptions concerning what it means to be human in modernity and examine companion or social robots at a moment of crisis and the substantive reorganisation of social reproduction wrought by neoliberal austerity. We end by calling for a reimagining of elder care, in which the care of our elders is radically revalued and where robots assist and support workers in their difficult and skilled labour of care.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".