G20, Social Science, Environmental Science, Economics, Econometrics and Finance Research Trend
Bibliographic record
Abstract
The G20 is a multilateral cooperation forum consisting of 19 major countries and the European Union (EU) which has a middle to high income class, developing countries to developed countries. G20 members consist of countries from various regions of the world. United States, Canada, Mexico, Argentina, Brazil, United Kingdom, Germany, Italy, France, Russia, South Africa, Saudi Arabia, Turkey, China, Japan, South Korea, India, Indonesia, Australia and the European Union. In the G20 Indonesia became one of the 5 strongest economies in Asia Pacific, including: (1) China GDP 14.72 trillion US dollars (2020) Area 9.597 million km2 Population 1.402 billion people (2020) China's largest economy from manufacturing, services, and agriculture. SCOPUS.COM analyze search results, Social Science, Environmental Science, Economics, Econometrics and Finance 22,997 documents result since 1974 to 2022, all field article. So today in good trend of research and publication in Social Science, Environmental Science, Economics, Econometrics and Finance. Compare top 15 affiliations, dominant affiliations from China and Malaysia, the best 3 affiliations are Beijing Institute of Technology 318 documents; Xi'an Jiaotong University 227 documents and Southwestern University of Finance and Economics 211 documents. Still no Indonesia research institution in top 15 of the list. Compare top 15 affiliations, the best 3 countries are China, USA and UK. In ASIA and ASEAN, Indonesia under China, Pakistan and Malaysia. So this is real challenge for Indonesia research institution to expand their research in Social Science, Environmental Science, Economics, Econometrics and Finance, trend in 2010-nowadays
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.029 | 0.060 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.097 | 0.094 |
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".