Bibliographic record
Abstract
At this roundtable discussion, researchers and policy makers talked broadly about the most important questions that had been answered by recent empirical research and which questions remained to be answered. They were asked to consider research and policy related to “Population aging and pressure points in a life course framework: disability and long term care.”\nValérie Émond is an scientific expert at the Quebec Public Health Institute. Over the past 10 years, she has worked at developing chronic disease surveillance for the province of Quebec using administrative data. After developing the model for diabetes surveillance, she is currently working on an integreted chronic disease surveillance model, with a research focus on the elderly and multimorbidity. She has a background degree in actuarial mathematics from Université Laval and a Masters degree in demography from Université de Montréal. She has also worked for the Canadian Institute for Health Information where she acquired an extensive knowledge of health administrative data across Canada.
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 | 0.094 | 0.115 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.031 | 0.033 |
| Open science | 0.011 | 0.018 |
| Research integrity | 0.130 | 0.070 |
| Insufficient payload (model declined to judge) | 0.057 | 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".