Bev Said “No”: Learning From Nursing Home Residents About Care Politics in Our Aging Society
Bibliographic record
Abstract
How do nursing home residents decide when, whether, or how to respond to their own and others' care needs when the need to do is constant? What can we learn from them about care politics in our aging society? Drawing on ethnographic research conducted in three long-term residential care homes in Ontario Canada, this article weaves approaches from the arts, humanities, and interpretive sociology to respond to these questions. Contextualizing nursing home residents' stories of care within broader sociocultural and political contexts, I consider how they develop critical and creative insights, not only about direct care or nursing home life but about moral, philosophical, and culturally significant questions relevant to care provision. As political actors engaged in a "politics of responsibility," they put work into navigating, negotiating, and making sense of their own and others' care needs in under-resourced contexts and in relation to circulating narratives about care, aging, and disability. Exposed to constant demands to care for others, residents' stories highlight the importance of expanding cultural narratives to embrace embodied differences or care needs, to help people to talk about their own needs or limits, and to organize care as a shared, collective responsibility.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.032 | 0.037 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.013 |
| 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".