Bibliographic record
Abstract
Most economists blame Canada's lackluster productivity performance in recent decades for sluggish growth in per capita incomes and a declining position in international rankings of economic well-being. Political leaders are reluctant to embrace a productivity agenda because of the public's confusion, indeed fear, of the subject. Many believe productivity is about working harder for less pay, or precisely the opposite of the economist's definition. Despite poor productivity growth, Canada remains a wealthy country. But there is ample reason for concern. Canada's level of productivity has slipped to 17th among OECD nations from third in the 1950s and 1960s. Unless our record is turned around quickly, Canadians' quality of life will stand still while other nations move ahead. This article summarizes the elements that are in common in most economists' recommendations on how to raise productivity in Canada. Some of the recommendations require governments to tackle issues such as removing interprovincial trade barriers and reforming employment insurance where firmly established interests would be rocked. The private sector would have to shed some complacency. But the pay-offs would be enormous. Economists have come together impressively on an action plan to raise productivity, now they need to hone their communications skills to convince the country to swallow the prescribed medicine.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 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".