The Politics of Chequebook Federalism: Can Electoral Considerations Affect Federal-Provincial Transfers?
Bibliographic record
Abstract
Canada’s equalization program is supposed to ensure that provinces that lack the same ability to raise revenue as other provinces, due to economic differences, are still able to provide their residents with roughly similar levels of public service. The equalization program itself is ostensibly based on a formulaic approach, with automatic equalization payments kicking in where and when they are needed, while federal social transfers to the provinces are, at least by name, purportedly intended to support the social spending needs of those provinces. This is how things are supposed to work, anyway. But both equalization payments and social transfers are, inevitably, arranged by federal politicians, and politicians have a natural tendency to behave politically. An analysis shows that, in many cases, the amount of money a province receives in federal transfers is correlated with the way that province voted during federal elections. In other words, when a province exhibited dominant support for the national party that controls the federal purse strings, that province often received a greater share of federal transfers. Where provinces were largely unsupportive in a federal election for the victorious party, they were more likely to see their share of federal transfers shrink. This dynamic ultimately defeats the real purpose of federal transfers, which are intended to assist based on need, not based on political support. When transfer programs are modified for reasons other than need, one might rightly worry about suboptimal use of scarce public resources, while publicly undermining the legitimacy of what may be, in principle, worthy federal programs. Protecting against the influence of politics in such programs is vital to maximizing their efficiency and retaining their credibility. These programs can be redesigned in ways that safeguard against political interference. Appointing an independent body to manage fiscal transfer programs could be an important first step, as would putting constraints on the ability of the federal government to impose sudden floors and ceilings on transfers, or to cut special side deals with individual provinces or regions. Politicians will always behave politically; it is important to find ways to keep them from letting politics distort the principles of federal-provincial transfers.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".