Essential Policy Intelligence | Conseils indispensables sur les politiques ECONOMIC GROWTH & INNOVATION Capital Needed: Canada Needs More Robust Business Investment
Bibliographic record
Abstract
Business investment per worker in 2014 in Canada is falling relative to the rest of the developed world and the United States. Ontario and Quebec have become the national laggards, with the lowest per-worker investment levels in Canada. In the energy and resources sectors, which have been leading Canada’s capital investment, the latest figures suggest a loss of momentum. Policymakers can and should boost private-sector investment, through such measures as prioritizing growth-friendly taxation, creating opportunities in infrastructure and electric power, and increasing the rewards for R&D and innovation. Every Canadian worker – from a manufacturing worker in Ontario, to a welder in the oil sands, to a lawyer in Montreal – needs tools, buildings and equipment. But workers in some sectors and some provinces are getting more new kit than others. And Canadian workers as a whole get less new physical capital than workers in similar countries. Recent figures suggest that, after several years of improved performance against international competitors, Canadian non-residential business investment per worker is again falling behind. Per-worker spending on new capital in Canada is lower than the average figure among reporting countries in the Organisation for Economic Co-operation and Development (OECD). Ontario and Quebec are of particular concern: the two provinces have – for the first time in at least three Many thanks to the reviewers of a previous draft of this paper and to reviewers of previous editions of this series. Many Organisation for Economic Co-operation and Development staff members helped us in interpreting their data, for which we are most grateful. Of course, any errors of data interpretation or otherwise are our own.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.026 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.030 | 0.005 |
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".