“The Mark on the Floor”: Alice Munro on Ageing and Alzheimer’s Disease in The Bear Came Over the Mountain and Sarah Polley’s Away From Her
Bibliographic record
Abstract
In Italy, as well as in most European countries, alarm over Alzheimer's does not seem to be as obsessive as it is in North America, perhaps with the exception of the UK, where echoes of the medical research and media campaign conducted in Anglophone cultural contexts are certainly wider: "Americans now fear Alzheimer's more than any other disease, even cancer, according to a survey from MetLife." (Goldman 2017: 4) In spite of the fact that ageing studies are gaining attention and are gathering academic strength and rigorous scientific scrutiny, the main preoccupation in Italy/Europe seems to be about the increasing number of elderly people, also due to a decreasing birth rate, and the general sanitary, sociological and political consequences of such a demographic turn:It seems reasonable to assume that the growing numbers of very old people will increase the numbers of physically dependent people, with a resultant increase in costs, especially arising from their need for care due to ill health.(Thane 2000: 483) The Bear Came over the Mountain by the Canadian short story writer Alice Munro (The New Yorker 1999), taken as an emblematic literary representation of such a social change and its consequences in the Canadian context ("by 2021 the number of Canadians with dementia will rise to
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".