support regular physical activity Dear Mr. Romanow,
Bibliographic record
Abstract
On behalf of the core 40 national, provincial/territorial and community health and active living organizations of the Coalition for Active Living, I wish to offer to you the expertise and passion of the members of the Coalition for Active Living as you consider the options and challenges facing Canada’s health care system. To this end please find attached a short brief that will help you to make the case for the inclusion of an enhanced role for physical activity in a renewed and vibrant health care system. The Coalition receives funding from Health Canada to work to ensure that the environments where Canadians live, learn, work and play support regular physical activity. As a result of a national consultation that was carried out one year ago, the Coalition developed a Six-Point Plan of Action that highlights the most pressing issues for key decision-makers like yourself when faced with the challenge of addressing issues related to diabetes, chronic disease prevention, obesity, and overall good health of our population. This Action Plan is included in the submission. The Coalition for Active Living is committed to addressing the barriers that limit Canadians’ participation in physical activity. Health Canada is committed to reduce the levels of
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.047 | 0.019 |
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".