e-brief New Tools for a Richer, Greener Future: Why Canadian Workers Need More
Bibliographic record
Abstract
Improving Canadians ’ prosperity depends critically on investment in new plant and equipment. By speeding the adoption of new technology, higher rates of capital investment make Canadian products more competitive, and raise living standards. Countries with more capital per worker have higher incomes per worker.1 The recent report of the Competition Review Panel underscored the importance of dynamic, productive firms. In a world where production organized along global value chains shifts easily across borders (Dymond and Hart 2008), and in which market-friendly policies and lower-wage workers are intensifying competition, Canadians need more state-of-the-art tools to preserve their competitive edge. New machines and equipment, moreover, are likely to cut waste, reduce environmental stress and raise living standards as well as produce better goods and services. Troublingly, the numbers on capital formation, both for Canada as a whole and for many provinces, tell a story of underperformance. This e-brief updates a series of studies by the Institute that place Canada’s capital investment performance in international perspective.2 Over the past decade, business-sector capital formation in Canada has been consistently below the average for the G7, and is forecast to underperform the average for other OECD countries over 2008 and 2009. Despite economic weakness and credit-market turmoil in the United States, Canada 1 Sala-i-Martin (1997) showed a positive relationship between economic growth and investment in equipment and structures. Abdi (2004) presented evidence for Canada. De Long and Summers (1991) found a strong relation between machinery and equipment investment and growth in a study of a large sample of countries. In the much-watched comparison of productivity between Canada and the United States, Rao (2003), Baldwin and Gu (2007) and Statistics Canada (2007) have identified capital intensity as a significant factor in the poor productivity performance north of the border.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.176 | 0.030 |
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".