Analysis of Artificial Intelligence Readiness Performances of G7 Countries: An Application with LOPCOW-based MARCOS Method
Bibliographic record
Abstract
The artificial intelligence (AI) readiness performance of major economies can significantly impact the global economy. Therefore, analyzing the AI readiness performance of these economies is of great importance. In this study, the AI readiness performances of G7 countries were assessed using the most recent Government Artificial Intelligence Readiness Index (GAIRI) data for 2023. The analysis revealed that the importance of GAIRI components varies by country, with Data and Infrastructure generally being the most significant components. The countries were ranked according to their AI readiness performances using the LOPCOW-based MARCOS method as follows: USA, United Kingdom, Canada, France, Japan, Germany, and Italy. Notably, Italy's AI readiness performance was below the average, indicating the need for improvement to enhance its contribution to the global economy. The method applied proved to be sensitive in sensitivity analysis, credible and reliable in comparative analysis, and robust and stable in simulation analysis.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".