Éditorial : Concrétisation de la vision. L’Étude longitudinale canadienne sur le vieillissement, une initiative stratégique des Instituts de recherche en santé du Canada
Bibliographic record
Abstract
ABSTRACT The Canadian Longitudinal Study on Aging has progressed from a vision initiated by the CIHR Institute of Aging in 2001 to a federally funded national research platform in 2008. The development of the CLSA protocol was enhanced through a series of international peer reviews, a multisectorial Steering Committee, and a CIHR Ethical, Legal, and Social Issues committee; each was essential to the excellence of the science and to making the CLSA relevant to multiple sectors. The CLSA research team, led by three co-principal investigators (Kirkland, Raina, and Wolfson), has developed a unique protocol focusing on aging from cell to society, designed to follow 50,000 people aged 45 to 85, for 20 years. A strategic partnership with Statistics Canada has been crucial to the development and launch of the CLSA. The CLSA will contribute to our understanding of transitions and trajectories within an aging population, and will differ from longitudinal studies of aging worldwide through the breadth of its scope, the early age of recruitment into the study (age 45), the ethno-cultural diversity of Canada’s population, and the potential to link collected data to health administrative data at the provincial level. The CIHR is a novel longitudinal population-based study that will be an unprecedented research resource underpinning multidisciplinary research and evidence-based decision making in aging in Canada.
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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 | 0.025 | 0.112 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.009 | 0.003 |
| Research integrity | 0.027 | 0.033 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".