Learning for the Past: How Canadian Fiscal Policies of the 1990s Can Be Applied Today
Bibliographic record
Abstract
This work provides a historical overview that identifies parallels between the fiscal challenges facing Canadian governments in the 1990s and those facing governments in 2011. It highlights how the federal government, as well as various provincial governments in the 1980s, failed to balance their budgets when they attempted to slow the growth in program spending and wait for revenues to rebound strongly enough to close the gap between spending and resources. But it wasn’t until the spending reductions of the 1990s that both the federal government and the provinces returned to fiscal balance and achieved declining debt and interest costs. As the November 2011 budget update from Ottawa showed, the federal government is following the same plan as governments of the 1980s and now doesn’t expect to balance its budget until 2015/16, a year later than originally anticipated. Worse, most provincial governments are poised to make the same budget mistake as Ottawa: relying on rosy revenue projections while attempting to slow the growth in spending. The study singles out Ontario and Quebec, Canada’s two largest provinces, as facing among the most serious debt and deficit problems while praising Saskatchewan as the only province currently showing a balanced budget. The authors conclude that wishing for revenue growth will not balance the budget, but real spending reductions, based on the successes of the 1990s, will. They outline the decisive steps that Ottawa and the provinces must take to rein in spending and set the Canadian economy to rights.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.028 | 0.010 |
| Scholarly communication | 0.020 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".