The Economic Competitiveness of Countries: A Principal Factors Approach
Why this work is in the frame
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Bibliographic record
Abstract
Competition is a very important preconditionwhich affects the effectiveness of development of national economy under the conditions of globalization. In classical economics, the competitiveness of countries is determined through production inputs. In the modern era of globalization, it appears that, besides quantifiable factors, qualitative influences or ‘soft’ factors such as political stability, government policies, quality of education, etc., are all important in determining competiveness. The World Economic Forum’s global competitiveness index and the IMD World Competitiveness Yearbook (WCY) are the two most widely used competitiveness indices. Using the same data as the WCY, Principal Components Analysis (PCA) is used in this analysis to develop indices of countries’ competitiveness. The procedure deals with first transforming the original variables to a new set of uncorrelated variables called Principal Components (PC). The new variables are linear combinations of the original variables, independent, and are derived in order of decreasing importance--the first PC accounts for as much as possible of the variation in the original data. We find that the WCY data collection methods could be simplified without compromising quality--which may encourage more countries to participate in the survey. Moreover, the approach developed in this study does not suffer from the same empirical limitations of past attempts to develop indices of the competitiveness of nations.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it