Fixing the Fiscal Imbalance: Turning GST Revenues Over to the Provinces in Exchange for Lower Transfers
Bibliographic record
Abstract
The central argument of this paper is that Canada should better align provincial own-source revenues with provincial expenditures by turning over the GST to the provinces while simultaneously reducing federal transfers. The paper begins by broadly outlining of Canada's present fiscal arrangements and explains why a reduction of cash transfers and realignment of federal and provincial tax rates – in short, a transfer of tax points – would benefit the federation. It reviews past tax point transfers and then addresses the question of why transferring GST revenues is superior to other alternatives using the principles of tax assignment as the guide. It lays out two comprehensive proposals for transferring GST revenues, with the key difference being the way in which the GST transfer is equalized across provinces. It shows the impact on Ottawa and the provinces of these two proposals for the 2009-2010 fiscal year. It highlights the benefits of these changes from a federal, provincial and taxpayer perspective with a special focus on one of Canada's major expenditure challenges what to do about public health spending.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".