Equipping Canadians for Success: A Shadow Budget for 2014
Bibliographic record
Abstract
The 2014 edition of the C.D. Howe Institute’s annual Shadow Federal Budget reinforces Ottawa’s near-term focus on returning to budgetary surplus, and presents a number of measures to foster growth in living standards over the longer term. Prudent forecasting and greater transparency in budgeting and government spending are two elements of the plan to achieve an early and durable surplus. Spending control is the third – with the dominant thrust being containing the cost of federal employment, and in particular the costs to taxpayers of federal pensions and other postretirement benefits. The Shadow Budget estimates that caps on taxpayers’ contributions to federal-employee pensions, the termination of indefinite banking of unused sick days, and a move to 50-50 cost-sharing of post-retirement health benefits can reduce federal spending by some $5.2 billion in fiscal years 2014/15 and 2015/16 alone. Shadow Budget measures to equip Canadians for success in the longer term fall under three broad headings: the skills of, and opportunities for, Canadian workers; saving and investment; and innovation and entrepreneurship. Measures to promote skills and opportunities include reducing the pressure of the federal government on Canadian labour markets, investing in new job-information and matching, shifting the tax base from income to consumption taxes, phasing out regionally differentiated Employment Insurance, and promoting longer worklife by increasing the age at which Canadians lose access to tax-deferred saving. The Shadow Budget contains several measures to improve the treatment of saving, and savings, in tax-deferred vehicles. It also proposes to promote investment by providing more uniform tax treatment of active foreign-investment income, and a regular rolling review of capital-consumption allowances. As for innovation and entrepreneurship, the Shadow Budget proposes a more favourable tax regime for intellectual property income and making academic excellence the key criterion in supporting basic research. It would reduce ongoing support to many Crown corporations, prepare a slimmed-down Canada Post for a competitive mail market, and create more market discipline for Crown lenders. It would also reduce tariffs, and create a more transparent system for reporting, reviewing and reducing tax expenditures that substitute for formal spending programs. The 2014 Shadow Budget builds on Canada’s relatively good post-crisis performance. It reinforces progress toward surpluses, and so protects Canadians from higher interest rates and reduces the claims of government borrowing on Canadian saving. And it supports economic growth with measures to better deploy Canada’s human capital, promote investment, and stimulate income-boosting innovation. In short, it provides the kind of fiscal leadership Canadians need from their federal government.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.046 | 0.013 |
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".