Reforming Canada's federal health‐care funding arrangements
Bibliographic record
Abstract
Abstract Federal health‐care funding has long been a source of policy debate in this country, a situation exacerbated recently by the COVID‐19 pandemic and the premiers' calls for a large expansion of the Canada Health Transfer. In this article, we explore four potential pathways policy‐makers might consider in order to improve federal health‐care funding. These potential pathways should allow policy‐makers to consider how to adapt to changing circumstances while addressing citizens' concerns and the demands of provincial/territorial governments. We do not support one or another of these policy pathways. Instead, we explain what they are and what impact they could have.
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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 | 0.026 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.020 | 0.007 |
| Scholarly communication | 0.017 | 0.004 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".