Payment where Payment is Due: Canada’s Federal Transfer System and a Needs-Based Solution to Health Transfer Spending
Bibliographic record
Abstract
Since the 1950s, federal transfers have been moulded and remoulded under practically every Prime Minister. The current iteration of transfers, specifically the 2014 implementation of equal-per-capita funding through the Canada Health Transfer, poses major problems to regional disparities, and arguably favours provinces that have high growth; this leaves poorer provinces, like the Maritimes, to make major cuts to provincial budgets in order to maintain the standards set out in the Canada Health Act. This paper explores the history of transfers, why transfers are necessary for Canadians, as well as the criticisms of the current system. Following this, it is recommended that a needs-based model for determining health transfers be adopted; specifically, the model developed by Marchildon and Mou that accounts for an aging population as well as one that is geographically dispersed. This paper provides a more contemporary analysis on federal transfers as they relate to the health care system. Additionally, it focuses somewhat on the issues New Brunswick is facing currently as, among other things, a result of inadequate funding from the federal government.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
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".