ANALYSIS OF EASE OF DOING BUSINESS PERFORMANCE OF G20 GROUP COUNTRIES: AN APPLICATION WITH STANDARD DEVIATION BASED ARAS METHOD
Bibliographic record
Abstract
Ease of doing business (EDB) strategies and activities of countries with large economies affect global economy, trade, employment and other dimensions related to economy. Therefore, measurement of EDB performances of countries with large economies is of great importance. In this context, in study, EDB performances of 19 countries in G20 for year 2020, the latest and most up to date EDB Index (EDBI) component values are measured by SD (Standard Deviation) based ARAS. In research, firstly, the most important EDBI component according to countries was determined as "resolving insolvency" with SD. Secondly, with SD based ARAS method, it was determined that first three countries with the highest performance in EDB were USA, South Korea, England, and last three countries were Argentina, Brazil, Saudi Arabia. In study, average EDB performance values of countries were also measured, and it was determined that countries with higher than average EDBI performance were USA, South Korea, England, Australia, Germany, Canada, Japan, Russia, China, France. According to result, it was evaluated that countries with below average EDB performance should increase their EDB performance to increase their contribution to global economy. In study, it was also concluded that EDBI can be explained by SD based ARAS method according to sensitivity, discrimination, correlation analyzes in terms of method.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".