Evaluation of Logistics Performances of G20 Countries Using SD-Based COPRAS and SAW Methods
Bibliographic record
Abstract
One of the important issues in the economic development of countries is their effectiveness in logistics activities. Countries gain competitive advantage by maintaining effective and efficient logistics processes. Therefore, determining logistics performance is important for both businesses and countries. The main aim of this study is to examine the logistics performances of countries in the context of G20 countries and to determine how they change over time. Within the framework of this aim, the Logistics Performance Index published by the World Bank has been used to determine the logistics performance of countries (LPI (2018) and LPI (2023)). Standard Deviation (SD) method has been used in weighting the criteria “customs, infrastructure, international shipments, logistics competence and quality, timeliness, tracing and tracking” included in the LPI and in determining the performance of G20 countries. Data for 2018 and 2023 have been examined using the methods COPRAS (Complex Proportional Assessment) and SAW (Simple Additive Weight). The results obtained from the methods have been compared with LPI (2018) and LPI (2023). As a result of the analysis, according to the COPRAS method, Germany, Japan, and the United Kingdom rank first in 2018, while the Russian Federation, Argentina and Brazil rank last, respectively. According to 2023 data, Germany ranks first according to both methods, while Canada and Japan follow Germany in line with the COPRAS method. According to the SAW method, Japan and Canada follow Germany. Russia and Argentina rank in last place in both methods, similar to the current index.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".