G20 Ülkelerinin COVID-19 Öncesi ve COVID-19 Dönemi Lojistik Performanslarının Kıyaslanması: MEREC ve CODAS Entegre Yaklaşımı
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it