Calculation of the main and sub-indicators of social power affecting the national power of countries and comparing the status of Iran with other countries
Bibliographic record
Abstract
Social power is of great importance among scholars of different sciences due to its importance in the sphere of individual and public life. On the other hand, this type of power can be considered as one of the dimensions of soft power that influences the national power of political-spatial structures. Therefore, the purpose of this study is to investigate the factors affecting social power and determine the coefficient of importance of each variable and finally rank countries accordingly. The research method has three main stages. In the first phase, library studies were written with reference to articles, books, and theoretical foundations of research. In the second phase, 77 internal and external experts referred to the field findings section to assess the significance of social variables affecting national power. Then, to measure the national qualitative variables, 30 foreign experts in social sciences, political sociology, etc. from India, France, China, Canada, etc. filled out a questionnaire designed online. Fischer maximum likelihood method was used to convert qualitative variables to quantitative. Then, considering the systematic and integrated approach of power, DEMATEL technique was used to determine the cause and effect relationships of the variables. Finally, 69 countries were ranked based on TOPSIS based on library and statistical data. The results showed that the United States with average (0.575), Switzerland (0.570), Norway (0.566), Sweden (0.526), Netherlands (0.522), New Zealand (0.487), England (0.482), Qatar (0.477), China (0.475) and India (0.468) rank first to tenth. The least significant were Vietnam (0.33), Nigeria (0.340), South Africa (0.347), Venezuela (0.353), Ghana (0.371), Ukraine (0.3718), Pakistan (0.376). Iran also ranks forty nine out of sixty-nine with an average (0.394).
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".