Analysis of Artificial Intelligence Readiness Performances of G7 Countries: An Application with LOPCOW-based MARCOS Method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it