A Conversation about Ageism: Time to Deinstitutionalize Long-Term Care?
Bibliographic record
Abstract
Ageism is arguably one of the least challenged forms of discrimination globally and manifests in many obvious and subtle ways. Situating our conversation within the context of COVID-19, we discuss peculiar and unchallenged forms of ageism in current times as well as the intersections with other forms of discrimination such as ableism, racism, sexism, and heterosexism. We highlight the limits of current understandings of ageism, specifically those that seek to identify positive aspects of ageism without appreciating how these forms of ageism reinforce inequalities among older adults. With regards to spatial manifestations of ageism, we explore the failure of critiques of institutionalization to include older people. Only in the context of “mass death” during COVID-19 has the public eye turned toward the problems of long-term residential care facilities as spaces of care, yet disabled, mad, and D/deaf people and allies have challenged the mass institutionalization of disabled people for decades, highlighting how physical and social segregation constitutes an obvious form of ableism. Institutions are notorious for their physical, spiritual, and emotional harms, but when it comes to residential long-term care for older people, especially older people living with dementia, responses to segregation and isolation have generally been ambivalent. Even aging studies scholars call for “transformation” but do not call for the elimination of large-scale institutions (e.g., Theurer et al.). We discuss this softer critique from aging studies, raising questions about whether institutionalized and segregated congregate living for older people is inherently discriminatory, and we consider the implications for families, health care administrators, researchers, and scholars working in the field of long-term residential 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 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 | 0.039 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.034 | 0.056 |
| Scholarly communication | 0.018 | 0.038 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.020 | 0.042 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".