Bibliographic record
Abstract
ity are on the policy agenda in most OECD countries, as governments seek to address prob-lems related to sluggish growth, such as weak employment growth, high unemployment or fiscal deficits. This agenda has also affected the work of the OECD. A comprehensive study of growth performance in the OECD area, includ-ing a set of policy recommendations, was pre-sented to the OECD Ministerial meeting in May 2001 (OECD, 2001). Further empirical findings and policy recommendations, focusing on the role of firm dynamics, regulatory factors and information and communications technol-ogy (ICT), were released in 2003 and 2004 (OECD, 2003a, 2003b, and 2004a). This article returns to the findings of these OECD studies and presents further empirical evidence on economic growth and productivity at the aggregate, industry and firm level. It par-ticularly focuses on the different growth experi-ences of the main OECD regions, notably Europe, the United States and Japan, and pays special attention to the position of Canada. The next section discusses aggregate growth patterns in the OECD area, examining the main factors affecting growth as well as some of the policies that may help strengthen growth. The third sec-tion focuses on multifactor productivity (MFP) growth, or the overall efficiency of labour and capital, and some of the factors that may have influenced the pick-up in MFP growth in certain OECD countries, such as investment in R&D and more rapid innovation, as well as the impacts of ICT use and firm turnover. The final section draws some conclusions. Growth Patterns
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".