Fiscal transfer in Canada: Drawing comparisons and lessons
Bibliographic record
Abstract
The Canadian system of fiscal transfers, which has been developed over a long period of time, has two central features: equalisation grants, which are constitutionally guaranteed, and the Canadian Health and Social Service Transfers (CHST). This paper examines the relevance and applicability of the Canadian system of intergovernmental transfers in the Indian case. Equalisation grants are meant to ensure that provinces have sufficient revenues to provide reasonably comparable levels of services at reasonably comparable levels of taxation. An elaborate `Representative Tax System' approach using individual revenue bases is used in Canada for determining the equalisation grants, although there has recently been a debate to use a more macro approach. The source by source approach is less practical in the Indian case for want of comparable and reliable information required for applying the method. A more practical alternative is the macro approach, which is adopted in India, but better indicators of fiscal capacity than those based on GSDP need to be used. In addition, the concept of ensuring that resources are available for maintaining the per capita expenditure of select basic services at certain levels among states, as attempted in Canada through the CHST transfers, is worth exploring.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.031 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".