Bibliographic record
Abstract
Led by innovation, leadership, transparency and excellence, the Institute of Aging provides a focal point for Canadian research on aging and pursues the fundamental goal of advancing knowledge in the field of aging to improve the quality of life and health of older Canadians. The Institute has carried out a range of important national and international strategic initiatives in aging, and has become influential in leveraging funding, enhancing research capacity and creating a new impetus in research on aging in Canada. The Institute engages and supports the scientific community, encourages interdisciplinary and integrative health research and fosters not only on the creation of new knowledge, but also on the translation of that knowledge into improved health, a strengthened health care system, and new health products and services for Canadians. The IA focuses on five priority areas of research: healthy and successful aging, biological mechanisms of aging, cognitive impairment in aging, aging and maintenance of autonomy, and finally, health services and policies relating to older people. The efforts of the IA are guided by five strategic orientations: to lead in the development and definition of strategic directions for Canadian research on aging ; to build research capacity in the field of aging ; to foster the dissemination, transfer and translation of research findings in policies, interventions, services and products ; to promote the importance of, and the need for, a research community in aging ; and to develop and support capacity-building and strategic research initiatives in the field of aging.
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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.029 | 0.007 |
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".