Productivity: What Is It? How Is It Measured? What Has Canada's Performance Been?
Bibliographic record
Abstract
This paper provides an overview of the productivity program at Statistics Canada and a brief description of Canada's productivity performance. The paper defines productivity and the various measures that are used to investigate different aspects of productivity growth. It describes the difference between partial productivity measures (such as labour productivity) and a more complete measure (multifactor productivity) and the advantages and disadvantages of each. The paper explains why productivity is important. It outlines how productivity growth fits into the growth accounting framework and how this framework is used to examine the various sources of economic growth. The paper briefly discusses the challenges that face statisticians in measuring productivity growth. It also provides an overview of Canada's long-term productivity performance and compares Canada to the United States - both in terms of productivity levels and productivity growth rates.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.022 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".