A COMPARATIVE ANALYSIS OF GLOBAL COMPETITIVENESS AMONG BRIC NATIONS: IMPLICATIONS FOR CHINA
Bibliographic record
Abstract
Since the acronym BRIC was coined in 2001, the world has touted Brazil, Russia, India and China as the emerging superpowers and engines of growth that would supersede the G7 economies and revive the sagging global economy. By 2010, the Big Four accounted for only 25% of the world’s gross national income despite owning over 25% of land area and over 40% of global population. This paper analyzes the global competitiveness of the BRIC nations over 15 years, in three five-year periods, and finds only China has shown stable growth. What then are the secrets to China’s growth and can China maintain growth? This paper then investigates the performance of China’s pillars of competitiveness and identifies the weak pillars, drawing attention to the issues and making recommendations for sustainable growth.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".