Bibliographic record
Abstract
In this paper, we provide an overview of the equalization grant system in Canada and the issues that have been raised concerning the reform of the fiscal transfer system. Any reforms to the equalization grant system have to balance three concerns — “efficiency” effects that arise through federal financing of transfers, and the incentive effects on provincial fiscal policies, “entitlement” to reasonably comparable public services at reasonably comparable levels of taxation, and “ownership” of resources and independence of fiscal policies by provincial governments. Five proposals for reform of the equalization system are discussed. With regard to the inclusion rate for resource revenues in equalization formula, we argue that the rate should be reduced from 50 per cent to 25 per cent and that ceiling on total equalization payments should be eliminated. We argue against the proposal to exempt from the calculation of equalization entitlements that are deposited in provincial sovereign wealth funds because this would not reduce total equalization entitlements in present value terms, it would be complex to implement if it extended to all forms of savings by provinces (such as debt reduction), and it would not alter the resource rich provinces’ incentives to save more of their resource revenues. We argue against a proposal to reduce CHT and CST to provinces with above average fiscal capacities because this would reduce their incentive to develop and tax their resources, and it would be counter to the purpose of these block grants, which is to reduce the vertical fiscal imbalance between the federal and the provincial governments. We review the Gusen (2012a) proto-type model for incorporating variations in costs and needs in the computation of the equalization entitlements and argue that this procedure seems feasible and merits further analysis.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".