The Economic Competitiveness of Countries: A Principal Factors Approach
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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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".