Bibliographic record
Abstract
A client's 'home' is wherever that person resides.It can be a private residence, supportive housing, retirement home, or just about anywhere except a hospital."OnTariO, Bringing 1 "It's been quite disturbing for us because everything here is set-up to create a home-like environment but a large number of our residents are admitted on the verge of palliative We're not really set up for a palliative population."inTerview wiTh care hOme sPiriTual healTh manager, 2014 1Few institutions have been surrounded by as much confusion, such gendered contradictions, and so many cultural anxieties as care homes for older adults, which bear the weight of cultural and economic uncertainties around population aging, changing perceptions of frailty and family ties, the meaning of dependence and independence, and fears of mortality.As one former care aide observes, "finding no one to blame for old age, why not blame those who house it?"(Tisdale xii).In their recent study Residential Care Transformed: Revisiting the Last Refuge, British gerontologists Julia Johnson, Sheena Rolph, and Randall Smith agree that "what people fear most is not residential care per se but ageing and the challenges of deep old age" (216).
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.178 | 0.068 |
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".