Analysis of G20 Countries in terms of Scientific Publication Performances
Bibliographic record
Abstract
The achievement of countries in generating scientific publications is also a reflection of their efforts in the scientific domain. The quantitative volume of these publications is not a criterion alone, but the fact that they are a source of inspiration for other scientists carrying out their studies in other countries is an important indicator in terms of evaluating the quality of publications. Based on this emphasis on scientific publications, this research aimed to assess the performance of nineteen G20 countries upon scientific publication data issued by The SCImago Journal & Country Rank and covering the years 1996-2022. The evaluation criteria do not only consist of the number of scientific documents, but also number of citable documents, number of citations, number of self-citations, number of citations per document and H-index values. Fuzzy Step-wise Weight Assessment Ratio Analysis (Fuzzy SWARA) method is employed to determine the priorities of the criteria with the participation of ten researchers from different scientific disciplines. As an outcome of the application of this method, the order of importance of the criteria is determined as H-index, number of citable documents, number of citations per document, number of citations, number of documents and self-citation. The performance order of nineteen countries is performed by using the CODAS-LN method, which includes a logarithmic normalization version of the COmbinative Distance-based ASsessment (CODAS) method and is a very convenient approach in cases where the data is not normally distributed. The results revealed that the United States has a superior position in terms of scientific publication performance, while the United Kingdom, Germany, Canada and France are aligned in the top five order. The consistency of the applied method is also confirmed by two different sensitivity analyses.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".