G20 Ülkelerinin COVID-19 Öncesi ve COVID-19 Dönemi Lojistik Performanslarının Kıyaslanması: MEREC ve CODAS Entegre Yaklaşımı
Bibliographic record
Abstract
Logistics is a sector contributing substantially to the economic and social development of a country. Countries benefit from the logistics performance index (LPI) published periodically by the World Bank to evaluate their logistics performance, identify weaknesses, and develop accordingly. The following six essential criteria are used to assess the countries’ logistics performance: customs, infrastructure, international shipments, logistics quality and competence, monitoring and tracking, and timeliness. Thus, this study aimed to evaluate the logistics performance of G20 countries before and during the coronavirus disease 2019 (COVID19) period. For this purpose, an integrated model based on the method based on the removal effects of criteria (MEREC) and the combinative distance-based assessment (CODAS), which are multi-criteria decision-making methods, was exploited. First, criterion weights were determined using the MEREC method. Second, the logistics performances of G20 countries were analyzed and compared using the CODAS method with respect to data from both before and during COVID-19 pandemic. The analysis results identified monitoring and tracing, customs clearance, international shipments, infrastructure, logistics quality, and adequacy and timing as the criteria weights during the pre-pandemic period and monitoring and tracing, international shipments, logistics quality and competence, customs, infrastructure, and timeliness during the pandemic period. Based on the CODAS method, the top five countries in the pre-pandemic period in the logistics performance ranking of the G20 countries were Germany, Japan, the UK, the United States of America, and France, respectively, and the top five countries in the ranking during the pandemic period were Germany, Canada, Japan, Spain, and France, respectively. In addition, to test the reliability and robustness of the model exploited, sensitivity and comparison analyses were performed. The results revealed that the pandemic affected the logistics performance of many countries.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".