Analysis of Business Sophistication Performance in G7 Countries: An Application of the MIEXCF-based CODAS Method
Bibliographic record
Abstract
The business sophistication (BS) performance of major economies significantly influences global BS and the global economy. Consequently, analyzing the BS performance of these economies is crucial. Therefore, the aim of this study is to evaluate the BS performance of G7 countries using the MIEXCF-based CODAS method, based on the most recent Global Innovation Index - Business Sophistication (GII-BS) criteria. Empirical findings reveal that "Knowledge Workers" and "Knowledge Absorption" are the most significant criteria for countries, according to the MIEXCF method. Therefore, G7 countries should place greater emphasis on the "Knowledge Workers" and "Knowledge Absorption" BS criteria to make a greater contribution to global business development and the economy. Furthermore, the BS performance of countries was measured using the MIEXCF-based CODAS method, and the performance rankings were determined as follows: USA, Japan, UK, France, Germany, Canada, and Italy. Additionally, the study calculated the average BS performance value, and it was found that Germany, Canada, and Italy had below-average BS performance values. Therefore, it is concluded that these countries need to improve their BS performance to contribute more to global BS and the global economy. Methodologically, sensitivity, comparative, and simulation analyses indicate that the MIEXCF-based CODAS method can be effectively used to measure the BS performance of countries using the GII-BS criteria.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".