National power measurement (case study: Oceania, Europa and North America)
Bibliographic record
Abstract
There is co-relation between national endowment and acceding to the health industry. The national power of each country reflects the level of influence at different levels of political, economic, and so on in order to advance a country’s major goals. National power is not a mere abstraction, but the national power of a country is the result of a set of variables that all lead to the formation of a nation’s national power. This article focuses on the national strength of the Western European Union (EU) countries of the United States, Canada, Mexico, Australia and New Zealand. As the national power of states determines the extent of their interactions and levels, it is necessary to investigate and measure this issue. In this paper, using the descriptive-analytical and mathematical methods of SAV and TOPSIS and finally averaging these two methods to measure the factors affecting the national power of countries based on the nine components of national power (political, economic, social, cultural, Educational, transboundary, space, territorial and military science).The results show that the United States, Canada, Germany, France, Australia, Luxembourg, Sweden, and Denmark ranksfirst.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".