Canadian Workers Need Better Tools: Rating Canada’s Performance
Bibliographic record
Abstract
Capital investment matters for Canadian prosperity. When businesses invest in machinery, equipment and structures, they equip their workers to create more and better goods and services, and earn higher salaries. Investment also boosts productivity economy-wide, allowing Canadians to pay for high-quality social goods as well. Yet the investment climate in Canada has changed in important ways in recent years; the factors that foster capital investment are in flux. The loonie’s rise relative to the US dollar has made imported equipment relatively less expensive. Changes in terms of trade have boosted the prospects of the resource sectors, while manufacturing is struggling. And Canada faces intensifying competition for investment both from the developed world and from rapidly growing developing countries. It is natural to wonder, therefore, how workers in Canada and the provinces are faring relative to their counterparts in other jurisdictions. In this e-brief, we update and extend previous work (Robson and Goldfarb 2004) on Canadian and provincial performance relative to other countries in the Organisation for Economic
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".