Thinking Through the Future of Care: Elder People’s Understandings of, and Feelings About, Robotic Interventions in Elder Care
Bibliographic record
Abstract
In the past several years interdisciplinary academic consideration has turned toward finding possible solutions to the increasing problem of unmet elder care requirements, one solution being the introduction of new robotic care technologies.This thesis addresses the future of elder care and the possibilities for change within the field of care -change that may no longer involve only human reorientation, but also non-human robotic transformation.Opinion varies on whether this potential for change will be inspired by technological advances, a growth in an elder population coupled with financial and labour constraints, or the consistent and ongoing devaluation of human care work.Whatever the inspiration may be, this work focuses on the fact that there exists an unknown future of caring, one that will certainly involve some mainstream manifestation of the non-human care robot, and collaboration between socially and scientifically focused researchers.Drawing on original research involving interviews with elder people regarding their understandings of and feelings about robotic interventions in elder care, this thesis presents the perspectives of a rarely consulted population and finds the future of non-human care to be marked by uncertainty and fear but also by an unexpected sense of hope in the companionship of robots.
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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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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".