Bibliographic record
Abstract
This paper presents estimates of effective multifactor productivity (MFP) growth for Canada, the United States, Australia, Japan and selected European Union (EU) countries, based on the EU KLEMS productivity database and the World Input-Output Tables. Effective MFP growth captures the impact of the productivity gains in upstream industries on the productivity growth and international competitiveness of domestic industries, thereby providing an appropriate measure of productivity growth and international competitiveness in the production of final demand products such as consumption, investment and export products. A substantial portion of MFP growth, especially for small, open economies such as Canada?s, is attributable to gains in the production of intermediate inputs in foreign countries. Productivity growth tends to be higher in investment and export products than for the production of consumption products. Technical progress and productivity growth in foreign countries have made a larger contribution to production growth in investment and export products than in consumption products. The analysis provides empirical evidence consistent with the hypothesis that effective MFP growth is a more informative relevant indicator of international competitiveness than is standard MFP growth.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".