The Key Challenge for Canadian Public Policy: Generating Inclusive and Sustainable Economic Growth
Bibliographic record
Abstract
Recent economic and fiscal projections produced by the Centre for the Study of Living Standards suggest that revenue growth over the next 23 years in most provinces and territories will be insufficient to maintain recent i ncreases in health expenditures while holding other spending constant on a real per capita basis. Motivated by these fiscal challenge s , we present a series of policy recommendations for Canada’s governments at all levels to foster greater economic growth. Higher GDP not only offers a means to raise government revenues, it also directly raises the well - being of Canadians. We consider options to boost economic growth in two broad ways. First, by boosting Canada’s productivity performance through policies pro moting private and public investment, education, technological innovation and diffusion, and trade. Second, by tapping into Canada’s underutilized labour supply, particularly by assisting women, older workers, persons with disabilities , Aboriginal people, and immigrants in successfully participating in the workforce. The recommendations in this report are guided by the Organization of Economic Co - operation and Development’ s green growth and inclusive growth frameworks and by the idea that government should take a more active role in supporting the economic activities of individuals and businesses.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.021 | 0.010 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".