ANALYSIS OF DEMOCRACY PERFORMANCES OF G7 COUNTRIES: AN APPLICATION WITH PSI METHOD
Bibliographic record
Abstract
With the advancement of democracy in a country, sustainable development, innovation, economic growth and progress can be achieved. Therefore, the progress of major economies in democracy can influence the global economy. In this context, the research measured the democracy performance of G7 countries, which account for more than half of global capital, using the Democracy Index (DI) components data created by The Economist Intelligence Unit (TEIU), the most recent and up-to-date data available, through the PSI multi-criteria decision-making (MCDM) method. According to the PSI (Preference Selection Index) method, the democracy performance of countries was ranked as Canada, Germany, the United Kingdom, Japan, France, Italy, and the USA. Furthermore, the average democracy performance value of countries was calculated, and it was observed that the countries with performance lower than this value were the United Kingdom, Japan, France, Italy, and the USA. Therefore, it is considered that the countries with lower-than-average democracy performance need to improve their democracy performance to contribute more to the global economy. Additionally, according to sensitivity, comparative, and simulation analyses in the research, it was concluded that the democracy performance of countries can be measured using the PSI method within the scope of the DI.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".