Adaptability, accountability and sustainability: Intergovernmental fiscal arrangements in Canada
Bibliographic record
Abstract
Transfers from Canada’s federal government to provinces, territories and local governments account for about one-fifth of the revenues of those governments, and about one-third of federal programme spending. While central governments in federations typically raise more, and sub-central governments typically raise less, than they spend directly, large gaps and transfers to bridge them strain the federal principle that governments at each level are sovereign in their respective spheres. Transfers can help achieve national-scale public goods, address spillovers among provinces, and support minimum standards for public services and other programmes across the country – yet Canada’s present system does not consistently reflect these purposes. The potential of large transfers to undermine accountability and foster unsustainable fiscal policies should inspire caution about their current size, and discourage expanding them. Demographic change will dampen the growth of government revenues in Canada and push programme spending up, particularly at the provincial level. Responding effectively will require a mix of tax increases and spending restraint from provinces and ideally partial pre-funding of programmes such as drug treatments and long-term care. Such reforms are likelier if the federal government limits growth in intergovernmental transfers, and reduces its draw on common revenue bases – the consumption base in particular – that the provinces will likely need to exploit more in the future.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".