Analysis of the Prosperity Performances of G7 Countries: An Application of the LOPCOW-based CRADIS Method
Bibliographic record
Abstract
The prosperity policies and strategies of major economies have the potential to significantly influence both the global economy and the prosperity of other nations. Therefore, the assessment of the prosperity performance of major economies holds paramount importance. In this context, the primary aim of this research is to evaluate the prosperity performance of G7 countries using the LOPCOW-based CRADIS method, leveraging sub-component values from the Legatum Prosperity Index. The secondary objective is to examine the relationship between a country's prosperity performance assessed through the LOPCOW-based CRADIS method and its quantifiability within the Legatum Prosperity Index (LPI) framework, as well as its associations with other Multi-Criteria Decision-Making (MCDM) methodologies. The findings reveal the ranking of countries' prosperity performance as follows: Germany, the United Kingdom, Canada, Japan, the United States, France, and Italy. Additionally, an assessment of the average prosperity performance of these countries highlights that the United States, France, and Italy perform below the established average. Consequently, it is imperative for these nations to enhance their prosperity performance to make a more substantial contribution to the global economy. Furthermore, sensitivity and discrimination analysis suggest that countries' prosperity performance can be quantified within the LPI framework. Another noteworthy observation is the strong resemblance of the LOPCOW-based CRADIS method to the MEREC-based CRADIS and the LOPCOW-based MARCOS methods
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".