Bibliographic record
Abstract
The beginning of the new millennium seems a good time to reflect upon issues in gerontology as many Western countries are experiencing population ageing and the challenges it entails. In comparison to Europe's 14 per cent average (Population Reference Bureau 2000), Canada is a relatively young country with only about 12 per cent of the population over age 65 (Statistics Canada 1999). Nonetheless, population ageing is a driving force in Canadian gerontology. In this paper we discuss current thinking in gerontology in Canada and how it reflects our approaches to research and development of public policy. We begin with an overview of national policy and research perspectives. We provide examples of research programmes addressing key issues: population ageing and the welfare state, rethinking independence, and quality of life issues from the Canadian perspective. Finally, we discuss the values that provide the context for the development of approaches to funding which affect the scope and direction of our research. Information sources include national policy documents, recent publications by Canadian gerontologists, and articles from the Canadian Journal on Aging 1998–2000.
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 | 0.020 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.024 | 0.027 |
| Scholarly communication | 0.021 | 0.008 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".