The Growth Story: Canada's Long-run Economic Performance and Prospects
Bibliographic record
Abstract
In this lead article, Peter Nicholson, who until recently served as advisor to the Secretary General at the OECD and is currently serving as policy advisor to the Prime Minister, Paul Martin, discusses the long-run economic performance, prospects in Canada, and policy priorities based on the framework and insights that emerged from the recent study of economic growth released by the OECD. He argues that Canada has performed remarkably well since the mid-1990s, and that by the pro-growth policy prescriptions developed by the OECD, Canada is doing most things right. However, Nicholson points out that our productivity gap relative to the United States is still large and growing and that finding ways to increase productivity growth is an increasing social and political necessity. Nicholson develops a scorecard on Canada's economic performance based on a three-star rating scheme. He gives Canada three stars for sound macro policies, human capital, and exposure to trade; two stars for productive investment; and one star, or perhaps a little better, for innovation. Despite this strong performance, Nicholson cautions against complacency, particularly given the demographic challenge the country will be facing in the years to come.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".