Righting the Course: A Shadow Federal Budget for 2018
Bibliographic record
Abstract
The C.D. Howe Institute’s Shadow Federal Budget for 2018 looks past the deficits the federal government has deliberately created in the near term, and urges the government to think longer term, with a framework that will reassure Canadians about the sustainability of fiscal policy while responding to sharper competitive pressure on trade and taxation. Balancing astute spending measures and cost savings with revenue generation can inspire confidence that the country is ready for the longer-term challenges of slower growth and an aging population – a good backdrop for measures that support economic growth and job creation, and promote opportunities for all Canadians. This Shadow Budget responds to competitive pressures from US tax reforms with some immediate measures, and lays the foundation for more fundamental reforms that will have a lasting impact on Canadian productivity and incomes. Two quick changes to accelerate write-offs of business investment will signal Canada’s readiness to restore some of our lost competitiveness for new investments. Longer term, establishment of an allowance for corporate equity that relieves ordinary returns to capital from corporate income tax would make Canada more attractive for domestic and foreign investors. This Shadow Budget holds the line on the federal government’s own operating costs and eliminates some poorly designed or targeted tax preferences. It improves environmentally motivated taxation by establishing a higher GST rate on transportation fuels and eliminating the aviation fuel tax. It reorients infrastructure spending toward projects on which the federal government can move quickly and efficiently. It further improves the transparency of government finances and the ability of legislators to control public money. Other policies advancing Canada’s openness to trade and competition, and supportive of higher student achievement, will further strengthen the country’s economic capacity. This Shadow Budget also expands individual opportunities by facilitating the movement of human talent to where job prospects are brighter and rewards greater, and by fostering more saving opportunities and income security for our seniors, now and in the future. On the spending side, this Shadow Budget proposes to dispose of non-core assets and increase private investment in infrastructure by selling selected airport leases, reduce punitive personal income taxes, continue to invest in education for Indigenous children, and provide a more generous tax treatment for nondiscretionary medical expenses. In summary, this Shadow Budget promotes economic growth with reforms that will attract investment, promote international trade, and encourage work and saving. It sets federal finances on a longer-term course back to balance, assuring Canadians that they can pursue their lives and work, save and invest with confidence.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.074 | 0.034 |
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".