Demographic Context, Policy and Practice Agendas, Research Opportunities
Bibliographic record
Abstract
Webcast sponsored by Irving K. Barber Learning Centre, and hosted by Green College.This presentation will place the aging of Canada's population in an international context. It will consider implications of population aging for health services and systems (especially in relation to cognitive impairment, mobility in aging, and home care); and it will address the research opportunities of more longitudinal lenses and emerging scientific perspectives. Anne Martin-Matthews has recently completed two terms (2004-2011) as the Scientific Director of the Institute of Aging, one of 13 national Institutes of the Canadian Institutes of Health Research. Since coming to UBC in 1998, she has held positions as Associate Dean Research, Associate Dean Strategic Initiatives, and Dean pro tem in the Faculty of Arts. She has been a member of the Department of Sociology since 2008. Under her leadership, the CIHR Institute of Aging led the development of the Canadian Longitudinal Study on Aging (CLSA), a 20 year study of 50,000 Canadians aged 45-85. The CLSA was launched in 2009. The Institute of Aging has also developed strategic initiatives on Cognitive Impairment in Aging, on Mobility in Aging, and on Health Services and Systems for an Aging Population. The Institute is an associate member of the ERA-AGE2, and participated in FUTURAGE, leading to the development of a European Roadmap for Research on Aging. International partnerships were developed with China, Japan, France and with the UK.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.041 | 0.004 |
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".